Qwen2.5-7B-Instruct

All results produced by QueryGym · fully reproducible!

30 method × retriever configurations using this LLM across BEIR, MS MARCO DL, and DL-HARD.
Click any row or the + button to expand. Tabs switch dataset context. The three steps (reformulate → retrieve → evaluate) update accordingly.

	Method	Retriever	ArguAna		DBPedia		FiQA		SciFact		COVID		News		BRIGHT — AOPS		BRIGHT — Biology		BRIGHT — Earth Science		BRIGHT — Economics		BRIGHT — LeetCode		BRIGHT — Pony		BRIGHT — Psychology		BRIGHT — Robotics		BRIGHT — Stack Overflow		BRIGHT — Sustainable Living		BRIGHT — TheoremQA Questions		BRIGHT — TheoremQA Theorems		DL-HARD		DL 2019		DL 2020
	Method	Retriever	nDCG@10	R@100	nDCG@10	R@100	nDCG@10	R@100	nDCG@10	R@100	nDCG@10	R@100	nDCG@10	R@100																									nDCG@10	R@1k	nDCG@10	R@1k	nDCG@10	R@1k
	csqe	BGE-base-en-v1.5	0.6231	0.9893	0.3826	0.4879	0.3939	0.7437	0.7415	0.9727	0.7862	0.1449	0.4360	0.5126	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3671	0.8348	0.7127	0.8803	0.6885	0.8850
methodcsqe llmQwen2.5-7B-Instruct retrieverBGE-base-en-v1.5 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method csqe \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-arguana.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-arguana-test run.txt Run id db84561b5bb02956 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-dbpedia-entity \ --method csqe \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-dbpedia-entity.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-dbpedia-entity-test run.txt Run id da17c968549a5fc6 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-fiqa \ --method csqe \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-fiqa.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-fiqa-test run.txt Run id 246fe58479a73dee · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-scifact \ --method csqe \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-scifact.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-scifact-test run.txt Run id cebe92a4d886c671 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-covid \ --method csqe \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-covid.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-covid-test run.txt Run id 1b3210148a98ea23 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-news \ --method csqe \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-news.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-news-test run.txt Run id 7767b310f88ba350 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.dlhard \ --method csqe \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ /mnt/data/son/Thesis/t5/data/dlhard/neutral_queries.tsv run.txt Run id ea85b70406c5f97f · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2019 \ --method csqe \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl19-passage run.txt Run id 8a50e76e3972faef · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2020 \ --method csqe \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl20-passage run.txt Run id 2ccc07bbc652dc9a · open full run detail →
	csqe	BM25	0.4008	0.9403	0.3767	0.5078	0.2200	0.5466	0.7183	0.9543	0.6757	0.1600	0.4504	0.5795	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3322	0.7913	0.6873	0.8921	0.6083	0.8596
methodcsqe llmQwen2.5-7B-Instruct retrieverBM25 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method csqe \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-arguana.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-arguana-test run.txt Run id a4b96e532713cf8c · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-dbpedia-entity \ --method csqe \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-dbpedia-entity.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-dbpedia-entity-test run.txt Run id 45f75d3dda634e3d · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-fiqa \ --method csqe \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-fiqa.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-fiqa-test run.txt Run id 86d33a6522c7bf66 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-scifact \ --method csqe \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-scifact.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-scifact-test run.txt Run id ed3f091219373725 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-covid \ --method csqe \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-covid.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-covid-test run.txt Run id 46831c7ce6869053 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-news \ --method csqe \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-news.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-news-test run.txt Run id 0966213446c0caca · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.dlhard \ --method csqe \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ /mnt/data/son/Thesis/t5/data/dlhard/neutral_queries.tsv run.txt Run id a669e73a9f865bf9 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2019 \ --method csqe \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"zs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl19-passage run.txt Run id 001ab6762f99c6b6 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2020 \ --method csqe \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl20-passage run.txt Run id 2b82b2cea4817e13 · open full run detail →
	csqe	SPLADE++	0.5100	0.9801	0.3661	0.4830	0.3035	0.6521	0.6765	0.9527	0.6096	0.1024	0.4079	0.4866	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3025	0.8057	0.6523	0.9089	0.6164	0.9039
methodcsqe llmQwen2.5-7B-Instruct retrieverSPLADE++ 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method csqe \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-arguana.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-arguana-test run.txt Run id e30beb7d6120032b · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-dbpedia-entity \ --method csqe \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-dbpedia-entity.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-dbpedia-entity-test run.txt Run id c645b63d148df699 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-fiqa \ --method csqe \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-fiqa.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-fiqa-test run.txt Run id 7c0e15a8a3ed05d7 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-scifact \ --method csqe \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-scifact.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-scifact-test run.txt Run id d991a771a4ec16c3 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-covid \ --method csqe \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-covid.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-covid-test run.txt Run id 6a5b94d82f568fbb · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-news \ --method csqe \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-news.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-news-test run.txt Run id bfcb1e13cc699a61 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.dlhard \ --method csqe \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ /mnt/data/son/Thesis/t5/data/dlhard/neutral_queries.tsv run.txt Run id 262563ce22f62374 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2019 \ --method csqe \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl19-passage run.txt Run id 38aa528bf5037635 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2020 \ --method csqe \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl20-passage run.txt Run id 0947529629fd12ec · open full run detail →
	genqr	BGE-base-en-v1.5	0.6262	0.9893	0.3426	0.4550	0.3716	0.7167	0.7254	0.9600	0.7608	0.1382	0.4526	0.4886	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3375	0.8235	0.6416	0.8381	0.6335	0.8395
methodgenqr llmQwen2.5-7B-Instruct retrieverBGE-base-en-v1.5 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method genqr \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-arguana.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-arguana-test run.txt Run id b24c9b804c181612 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-dbpedia-entity \ --method genqr \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-dbpedia-entity.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-dbpedia-entity-test run.txt Run id c476c455fc59742a · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-fiqa \ --method genqr \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-fiqa.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-fiqa-test run.txt Run id ee23d7e703477508 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-scifact \ --method genqr \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-scifact.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-scifact-test run.txt Run id 67eaa2212fa021d7 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-covid \ --method genqr \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-covid.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-covid-test run.txt Run id eee82c9bce235b65 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-news \ --method genqr \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-news.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-news-test run.txt Run id 9655b2449c4788ee · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.dlhard \ --method genqr \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ /mnt/data/son/Thesis/t5/data/dlhard/neutral_queries.tsv run.txt Run id 3d6554e1ec12f297 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2019 \ --method genqr \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl19-passage run.txt Run id 6c1ae893bae633f1 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2020 \ --method genqr \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl20-passage run.txt Run id 2da66581b52f7858 · open full run detail →
	genqr	BM25	0.4339	0.9523	0.2876	0.4203	0.2041	0.5057	0.6919	0.9413	0.6523	0.1522	0.4295	0.5580	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.2006	0.6458	0.4334	0.7860	0.3857	0.7740
methodgenqr llmQwen2.5-7B-Instruct retrieverBM25 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method genqr \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-arguana.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-arguana-test run.txt Run id 817c54fef880fca4 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-dbpedia-entity \ --method genqr \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-dbpedia-entity.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-dbpedia-entity-test run.txt Run id 531a793785efac05 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-fiqa \ --method genqr \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-fiqa.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-fiqa-test run.txt Run id 881e0abf3b724af9 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-scifact \ --method genqr \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-scifact.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-scifact-test run.txt Run id e0436df6d059f504 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-covid \ --method genqr \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-covid.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-covid-test run.txt Run id fcf3c5eebc8142eb · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-news \ --method genqr \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-news.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-news-test run.txt Run id 9c3073daddc881ec · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.dlhard \ --method genqr \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"variants","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ /mnt/data/son/Thesis/t5/data/dlhard/neutral_queries.tsv run.txt Run id e80f5421465393eb · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2019 \ --method genqr \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"variants","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl19-passage run.txt Run id 0a66258aa59131ae · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2020 \ --method genqr \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"variants","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl20-passage run.txt Run id 8a3ae62556bd13a7 · open full run detail →
	genqr	SPLADE++	0.5211	0.9851	0.3703	0.5386	0.3057	0.6309	0.6942	0.9297	0.7060	0.1263	0.3950	0.4527	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3386	0.8000	0.6449	0.8870	0.6115	0.8989
methodgenqr llmQwen2.5-7B-Instruct retrieverSPLADE++ 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method genqr \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-arguana.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-arguana-test run.txt Run id 5490e12acba323e4 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-dbpedia-entity \ --method genqr \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-dbpedia-entity.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-dbpedia-entity-test run.txt Run id 9f6894dc79d6fc31 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-fiqa \ --method genqr \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-fiqa.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-fiqa-test run.txt Run id 284c21e5d24c5131 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-scifact \ --method genqr \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-scifact.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-scifact-test run.txt Run id 0964b93d4ba47353 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-covid \ --method genqr \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-covid.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-covid-test run.txt Run id 6b168618c3402fbc · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-news \ --method genqr \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-news.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-news-test run.txt Run id e24196fcf6955d28 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.dlhard \ --method genqr \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ /mnt/data/son/Thesis/t5/data/dlhard/neutral_queries.tsv run.txt Run id 91bac4d1f65b2f8b · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2019 \ --method genqr \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl19-passage run.txt Run id c56507d9c77300dd · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2020 \ --method genqr \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl20-passage run.txt Run id 303a213cc7bd8d75 · open full run detail →
	genqr_ensemble	BGE-base-en-v1.5	0.6196	0.9900	0.3462	0.4644	0.3792	0.7180	0.7375	0.9667	0.7754	0.1379	0.4589	0.5172	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3713	0.8356	0.6661	0.8520	0.6700	0.8582
methodgenqr_ensemble llmQwen2.5-7B-Instruct retrieverBGE-base-en-v1.5 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method genqr_ensemble \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-arguana.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-arguana-test run.txt Run id c25391a0bb85fed7 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-dbpedia-entity \ --method genqr_ensemble \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-dbpedia-entity.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-dbpedia-entity-test run.txt Run id 87b99d2d688bf22c · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-fiqa \ --method genqr_ensemble \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-fiqa.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-fiqa-test run.txt Run id 4d476856ccf27e28 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-scifact \ --method genqr_ensemble \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-scifact.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-scifact-test run.txt Run id 56b4e3412621417a · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-covid \ --method genqr_ensemble \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-covid.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-covid-test run.txt Run id f966a79fde5ed9ec · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-news \ --method genqr_ensemble \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-news.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-news-test run.txt Run id 24e2b17a1c11b518 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.dlhard \ --method genqr_ensemble \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ /mnt/data/son/Thesis/t5/data/dlhard/neutral_queries.tsv run.txt Run id 5dbc305b1a2674fe · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2019 \ --method genqr_ensemble \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl19-passage run.txt Run id 317413877c60ab2b · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2020 \ --method genqr_ensemble \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl20-passage run.txt Run id 671684f9e1f87ebe · open full run detail →
	genqr_ensemble	BM25	0.4187	0.9566	0.3464	0.4916	0.2075	0.5114	0.7035	0.9476	0.6780	0.1745	0.4367	0.6031	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.2429	0.7210	0.4512	0.7952	0.4896	0.8164
methodgenqr_ensemble llmQwen2.5-7B-Instruct retrieverBM25 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method genqr_ensemble \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-arguana.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-arguana-test run.txt Run id e49dd54b6f58c3cd · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-dbpedia-entity \ --method genqr_ensemble \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-dbpedia-entity.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-dbpedia-entity-test run.txt Run id e5190f2e71794feb · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-fiqa \ --method genqr_ensemble \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-fiqa.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-fiqa-test run.txt Run id 3070244b6a934e3e · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-scifact \ --method genqr_ensemble \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-scifact.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-scifact-test run.txt Run id fdd2a2c42bfdd898 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-covid \ --method genqr_ensemble \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-covid.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-covid-test run.txt Run id d24edd01bf1e0629 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-news \ --method genqr_ensemble \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-news.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-news-test run.txt Run id babe70f0dbdbc64c · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.dlhard \ --method genqr_ensemble \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"variants","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ /mnt/data/son/Thesis/t5/data/dlhard/neutral_queries.tsv run.txt Run id 7c2a243a2ffb9ede · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2019 \ --method genqr_ensemble \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl19-passage run.txt Run id e94186b450bd891b · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2020 \ --method genqr_ensemble \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"variants","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl20-passage run.txt Run id fb64d41cd58d8e73 · open full run detail →
	genqr_ensemble	SPLADE++	0.5180	0.9815	0.3589	0.5194	0.2882	0.6249	0.6964	0.9460	0.6420	0.1117	0.4049	0.4814	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3292	0.8005	0.5948	0.8824	0.6307	0.9020
methodgenqr_ensemble llmQwen2.5-7B-Instruct retrieverSPLADE++ 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method genqr_ensemble \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-arguana.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-arguana-test run.txt Run id aa0a09c2d60fb379 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-dbpedia-entity \ --method genqr_ensemble \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-dbpedia-entity.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-dbpedia-entity-test run.txt Run id 0952aa4806a53daa · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-fiqa \ --method genqr_ensemble \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-fiqa.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-fiqa-test run.txt Run id 0f7fecda7817ce82 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-scifact \ --method genqr_ensemble \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-scifact.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-scifact-test run.txt Run id ec9647c785553bac · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-covid \ --method genqr_ensemble \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-covid.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-covid-test run.txt Run id 23d94602e1b554fc · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-news \ --method genqr_ensemble \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-news.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-news-test run.txt Run id f03a3866e19eaf1b · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.dlhard \ --method genqr_ensemble \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ /mnt/data/son/Thesis/t5/data/dlhard/neutral_queries.tsv run.txt Run id e525464e0a9d8b0c · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2019 \ --method genqr_ensemble \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl19-passage run.txt Run id 720cb5b5982a9dc3 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2020 \ --method genqr_ensemble \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl20-passage run.txt Run id d3c9513f7c3251ba · open full run detail →
	lamer	BGE-base-en-v1.5	0.6195	0.9908	0.3900	0.4838	0.3981	0.7318	0.7466	0.9733	0.7843	0.1360	0.4517	0.4753	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3788	0.8315	0.7113	0.8668	0.6825	0.8940
methodlamer llmQwen2.5-7B-Instruct retrieverBGE-base-en-v1.5 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method lamer \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-arguana.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-arguana-test run.txt Run id a50f451a6405c42d · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-dbpedia-entity \ --method lamer \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-dbpedia-entity.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-dbpedia-entity-test run.txt Run id 667de708d3cde181 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-fiqa \ --method lamer \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-fiqa.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-fiqa-test run.txt Run id 5de7b3c84e18b6cf · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-scifact \ --method lamer \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-scifact.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-scifact-test run.txt Run id c3907dd559a87bc4 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-covid \ --method lamer \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-covid.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-covid-test run.txt Run id 88560f5e959c6818 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-news \ --method lamer \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-news.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-news-test run.txt Run id 9f17a3b07e7400e0 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.dlhard \ --method lamer \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ /mnt/data/son/Thesis/t5/data/dlhard/neutral_queries.tsv run.txt Run id f30bba5741441870 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2019 \ --method lamer \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl19-passage run.txt Run id 73a846cf910b84f8 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2020 \ --method lamer \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl20-passage run.txt Run id 230e2bbca7a7fd18 · open full run detail →
	lamer	BM25	0.4063	0.9388	0.3896	0.5139	0.2337	0.5558	0.7140	0.9593	0.6955	0.1704	0.4424	0.5960	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3570	0.7633	0.6602	0.8553	0.6322	0.8933
methodlamer llmQwen2.5-7B-Instruct retrieverBM25 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method lamer \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-arguana.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-arguana-test run.txt Run id 78a09817091818d1 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-dbpedia-entity \ --method lamer \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-dbpedia-entity.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-dbpedia-entity-test run.txt Run id d86dc93cb8e496dc · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-fiqa \ --method lamer \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-fiqa.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-fiqa-test run.txt Run id 4ae79ecd932684fd · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-scifact \ --method lamer \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-scifact.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-scifact-test run.txt Run id eb8c999ba4df568e · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-covid \ --method lamer \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-covid.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-covid-test run.txt Run id ffeb0dbe30c15130 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-news \ --method lamer \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-news.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-news-test run.txt Run id 191f3146ee820605 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.dlhard \ --method lamer \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ /mnt/data/son/Thesis/t5/data/dlhard/neutral_queries.tsv run.txt Run id 2887a055720c6629 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2019 \ --method lamer \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl19-passage run.txt Run id 67f45dc24ca2c49b · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2020 \ --method lamer \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl20-passage run.txt Run id 4d996428a216f673 · open full run detail →
	lamer	SPLADE++	0.5148	0.9794	0.3499	0.4799	0.2944	0.6487	0.6651	0.9560	0.6339	0.1002	0.3967	0.4728	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3280	0.7917	0.6465	0.8654	0.6076	0.9213
methodlamer llmQwen2.5-7B-Instruct retrieverSPLADE++ 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method lamer \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-arguana.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-arguana-test run.txt Run id 0616194d13348b61 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-dbpedia-entity \ --method lamer \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-dbpedia-entity.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-dbpedia-entity-test run.txt Run id 327fda84b730628b · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-fiqa \ --method lamer \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-fiqa.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-fiqa-test run.txt Run id 6e3f251e13c96034 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-scifact \ --method lamer \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-scifact.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-scifact-test run.txt Run id 13886968f236369d · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-covid \ --method lamer \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-covid.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-covid-test run.txt Run id e760de4c23be40a5 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-news \ --method lamer \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-news.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-news-test run.txt Run id 0fd33ea475108659 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.dlhard \ --method lamer \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ /mnt/data/son/Thesis/t5/data/dlhard/neutral_queries.tsv run.txt Run id dbee4e6ccdf98310 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2019 \ --method lamer \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl19-passage run.txt Run id 66711eb3e485ecb1 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2020 \ --method lamer \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl20-passage run.txt Run id ebdfca59bc737639 · open full run detail →
	mugi	BGE-base-en-v1.5	0.6213	0.9922	0.4106	0.5195	0.4130	0.7456	0.7449	0.9767	0.8071	0.1406	0.4648	0.5142	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3619	0.8495	0.6869	0.8781	0.6888	0.8823
methodmugi llmQwen2.5-7B-Instruct retrieverBGE-base-en-v1.5 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method mugi \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-arguana.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-arguana-test run.txt Run id 8a090846f802a1b9 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-dbpedia-entity \ --method mugi \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-dbpedia-entity.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-dbpedia-entity-test run.txt Run id 7efc74d3d7b42740 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-fiqa \ --method mugi \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-fiqa.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-fiqa-test run.txt Run id 32a6aaa4f19280f2 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-scifact \ --method mugi \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-scifact.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-scifact-test run.txt Run id fbae23c6b1d00855 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-covid \ --method mugi \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-covid.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-covid-test run.txt Run id ed4abc42a91d4fc6 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-news \ --method mugi \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-news.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-news-test run.txt Run id ba319ae357c14074 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.dlhard \ --method mugi \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ /mnt/data/son/Thesis/t5/data/dlhard/neutral_queries.tsv run.txt Run id 7087d436683edb25 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2019 \ --method mugi \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl19-passage run.txt Run id d808a5c2486f3fcb · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2020 \ --method mugi \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl20-passage run.txt Run id c56c984559f3e98f · open full run detail →
	mugi	BM25	0.3926	0.9381	0.4006	0.5114	0.2368	0.5652	0.7063	0.9627	0.6771	0.1628	0.4436	0.5767	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3173	0.7707	0.6394	0.8732	0.6069	0.8882
methodmugi llmQwen2.5-7B-Instruct retrieverBM25 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method mugi \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-arguana.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-arguana-test run.txt Run id 546ff3396d14cb08 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-dbpedia-entity \ --method mugi \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-dbpedia-entity.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-dbpedia-entity-test run.txt Run id 08d0f86de18246a2 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-fiqa \ --method mugi \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-fiqa.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-fiqa-test run.txt Run id 687e67dc791532ef · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-scifact \ --method mugi \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-scifact.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-scifact-test run.txt Run id 357679b380ca5c0f · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-covid \ --method mugi \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-covid.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-covid-test run.txt Run id 31d81c8575bfaaad · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-news \ --method mugi \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-news.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-news-test run.txt Run id 88d0e4a8540d28d0 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.dlhard \ --method mugi \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ /mnt/data/son/Thesis/t5/data/dlhard/neutral_queries.tsv run.txt Run id 646e8a471d7c7a31 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2019 \ --method mugi \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl19-passage run.txt Run id 0bd45569e0452bd6 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2020 \ --method mugi \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl20-passage run.txt Run id ed52ae7274db797c · open full run detail →
	mugi	SPLADE++	0.5101	0.9787	0.3600	0.4989	0.2953	0.6597	0.6665	0.9593	0.6547	0.1045	0.4001	0.4725	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.2642	0.8028	0.5773	0.8929	0.5527	0.9104
methodmugi llmQwen2.5-7B-Instruct retrieverSPLADE++ 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method mugi \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-arguana.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-arguana-test run.txt Run id 1543aaf2b16b0616 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-dbpedia-entity \ --method mugi \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-dbpedia-entity.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-dbpedia-entity-test run.txt Run id 35d99cd527f8f873 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-fiqa \ --method mugi \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-fiqa.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-fiqa-test run.txt Run id 9a3a2408466e6ceb · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-scifact \ --method mugi \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-scifact.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-scifact-test run.txt Run id 1a50c50e259858ab · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-covid \ --method mugi \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-covid.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-covid-test run.txt Run id 266e1d1425d0563f · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-news \ --method mugi \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-news.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-news-test run.txt Run id 8d15e8f1910cdbc3 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.dlhard \ --method mugi \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ /mnt/data/son/Thesis/t5/data/dlhard/neutral_queries.tsv run.txt Run id a3c8453a6b8584b7 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2019 \ --method mugi \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl19-passage run.txt Run id e0b6047be3d4daf2 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2020 \ --method mugi \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl20-passage run.txt Run id 57db88c60c930ee5 · open full run detail →
	qa_expand	BGE-base-en-v1.5	0.6208	0.9900	0.3731	0.4872	0.3837	0.7309	0.7434	0.9583	0.7668	0.1378	0.4406	0.4862	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3418	0.8267	0.6740	0.8469	0.6541	0.8606
methodqa_expand llmQwen2.5-7B-Instruct retrieverBGE-base-en-v1.5 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method qa_expand \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-arguana.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-arguana-test run.txt Run id 6c2376aad9540469 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-dbpedia-entity \ --method qa_expand \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-dbpedia-entity.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-dbpedia-entity-test run.txt Run id 8b26539998c057e9 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-fiqa \ --method qa_expand \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-fiqa.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-fiqa-test run.txt Run id 27ba82fb04a9c256 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-scifact \ --method qa_expand \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-scifact.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-scifact-test run.txt Run id 72c5ff5490e6a425 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-covid \ --method qa_expand \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-covid.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-covid-test run.txt Run id 99d488aea38c4d7b · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-news \ --method qa_expand \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-news.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-news-test run.txt Run id 40b80f20a48019dd · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.dlhard \ --method qa_expand \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ /mnt/data/son/Thesis/t5/data/dlhard/neutral_queries.tsv run.txt Run id 2b9bd4d12fd57376 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2019 \ --method qa_expand \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl19-passage run.txt Run id 686aca7450ed4ffc · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2020 \ --method qa_expand \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl20-passage run.txt Run id fc2e9cf62ba68a18 · open full run detail →
	qa_expand	BM25	0.3940	0.9324	0.3338	0.4669	0.2234	0.5488	0.6857	0.9347	0.6729	0.1569	0.4340	0.5419	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.2892	0.7746	0.5553	0.7976	0.5654	0.8454
methodqa_expand llmQwen2.5-7B-Instruct retrieverBM25 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method qa_expand \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-arguana.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-arguana-test run.txt Run id 0b1063e73f0d4f40 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-dbpedia-entity \ --method qa_expand \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-dbpedia-entity.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-dbpedia-entity-test run.txt Run id 1ed19024d51ac455 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-fiqa \ --method qa_expand \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-fiqa.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-fiqa-test run.txt Run id 1d90675189dc6500 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-scifact \ --method qa_expand \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-scifact.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-scifact-test run.txt Run id 2442e15fb0f2b05b · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-covid \ --method qa_expand \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-covid.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-covid-test run.txt Run id ea0649fcdfbad201 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-news \ --method qa_expand \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-news.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-news-test run.txt Run id e5700ba0f20ac0d3 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.dlhard \ --method qa_expand \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ /mnt/data/son/Thesis/t5/data/dlhard/neutral_queries.tsv run.txt Run id e9bc830a47e74db3 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2019 \ --method qa_expand \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl19-passage run.txt Run id ffb3f7122f3c8b47 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2020 \ --method qa_expand \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl20-passage run.txt Run id 13736e30246456de · open full run detail →
	qa_expand	SPLADE++	0.5170	0.9829	0.3613	0.5111	0.2978	0.6387	0.6616	0.9547	0.6431	0.1103	0.3910	0.4548	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3143	0.8305	0.6574	0.8890	0.6156	0.8945
methodqa_expand llmQwen2.5-7B-Instruct retrieverSPLADE++ 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method qa_expand \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-arguana.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-arguana-test run.txt Run id 86132c2ba06bce75 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-dbpedia-entity \ --method qa_expand \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-dbpedia-entity.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-dbpedia-entity-test run.txt Run id 3a815069c7c06d46 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-fiqa \ --method qa_expand \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-fiqa.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-fiqa-test run.txt Run id 798e6395318b8632 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-scifact \ --method qa_expand \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-scifact.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-scifact-test run.txt Run id 4f85ddf666162891 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-covid \ --method qa_expand \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-covid.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-covid-test run.txt Run id 319d5e8617b6f81f · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-news \ --method qa_expand \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-news.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-news-test run.txt Run id bd558adf58708c17 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.dlhard \ --method qa_expand \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ /mnt/data/son/Thesis/t5/data/dlhard/neutral_queries.tsv run.txt Run id bc04280bf42f1888 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2019 \ --method qa_expand \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl19-passage run.txt Run id e7cb3389f68a82b0 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2020 \ --method qa_expand \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl20-passage run.txt Run id 5e53dafc053c231b · open full run detail →
	Q2D (COT)	BGE-base-en-v1.5	0.6195	0.9893	0.3498	0.4463	0.3896	0.7244	0.7336	0.9667	0.7769	0.1386	0.4295	0.4584	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3391	0.8300	0.6561	0.8397	0.6302	0.8573
methodQ2D (COT) llmQwen2.5-7B-Instruct retrieverBGE-base-en-v1.5 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"cot","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-arguana.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-arguana-test run.txt Run id ec6f2f0bfbe11ee2 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-dbpedia-entity \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"cot","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-dbpedia-entity.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-dbpedia-entity-test run.txt Run id 6a52c0ee2582fae6 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-fiqa \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"cot","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-fiqa.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-fiqa-test run.txt Run id 6dc10719e7bb31f5 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-scifact \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"cot","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-scifact.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-scifact-test run.txt Run id 5ea72635a9d00f04 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-covid \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"cot","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-covid.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-covid-test run.txt Run id adc7bbcd1610ce54 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-news \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"cot","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-news.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-news-test run.txt Run id 2bc2b50fcc191f3e · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.dlhard \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"cot","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ /mnt/data/son/Thesis/t5/data/dlhard/neutral_queries.tsv run.txt Run id b7a9e08fb17266de · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2019 \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"cot","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl19-passage run.txt Run id 3f623104924a5b28 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2020 \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"cot","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl20-passage run.txt Run id fa5477286d9d73fd · open full run detail →
	Q2D (ZS)	BGE-base-en-v1.5	0.6183	0.9893	0.3932	0.4932	0.4011	0.7311	0.7520	0.9633	0.8220	0.1440	0.4537	0.5067	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3675	0.8255	0.6907	0.8584	0.6617	0.8566
methodQ2D (ZS) llmQwen2.5-7B-Instruct retrieverBGE-base-en-v1.5 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"zs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-arguana.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-arguana-test run.txt Run id bafc4cbb90eefce9 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-dbpedia-entity \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"zs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-dbpedia-entity.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-dbpedia-entity-test run.txt Run id 712d408f9eac111e · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-fiqa \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"zs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-fiqa.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-fiqa-test run.txt Run id 51fa68087aa1a68f · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-scifact \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"zs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-scifact.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-scifact-test run.txt Run id 8c0e06b931030ec5 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-covid \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"zs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-covid.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-covid-test run.txt Run id 761fe758afbb68f9 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-news \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"zs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-news.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-news-test run.txt Run id ec056ac8f1c3ec27 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.dlhard \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train","mode":"zs"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ /mnt/data/son/Thesis/t5/data/dlhard/neutral_queries.tsv run.txt Run id a34860108901112a · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2019 \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train","mode":"zs"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl19-passage run.txt Run id f24a87851c5feeb8 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2020 \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train","mode":"zs"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl20-passage run.txt Run id 65c95fd35ef4fcd1 · open full run detail →
	Q2D (FS)	BGE-base-en-v1.5	0.6207	0.9886	0.3922	0.4865	0.3866	0.7308	0.7454	0.9567	0.7922	0.1388	0.4627	0.5133	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3628	0.8348	0.6776	0.8535	0.6402	0.8578
methodQ2D (FS) llmQwen2.5-7B-Instruct retrieverBGE-base-en-v1.5 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"fs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-arguana.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-arguana-test run.txt Run id 41b5e0f7064b4e35 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-dbpedia-entity \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"fs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-dbpedia-entity.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-dbpedia-entity-test run.txt Run id 274e2e975743aeec · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-fiqa \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"fs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-fiqa.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-fiqa-test run.txt Run id 8da7f8d35605c787 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-scifact \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"fs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-scifact.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-scifact-test run.txt Run id 1dae1477286b52d5 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-covid \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"fs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-covid.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-covid-test run.txt Run id 5aab29923d04055a · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-news \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"fs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-news.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-news-test run.txt Run id bdfae3d04daad047 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.dlhard \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"fs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ /mnt/data/son/Thesis/t5/data/dlhard/neutral_queries.tsv run.txt Run id be105f107361fac6 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2019 \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"fs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl19-passage run.txt Run id 4b08ed9e45b752ac · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2020 \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"fs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl20-passage run.txt Run id d767c686a44652a9 · open full run detail →
	Q2D (COT)	BM25	0.4011	0.9360	0.3669	0.4809	0.2405	0.5544	0.7096	0.9427	0.6997	0.1620	0.4349	0.5616	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3044	0.7815	0.6074	0.8585	0.5802	0.8684
methodQ2D (COT) llmQwen2.5-7B-Instruct retrieverBM25 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"cot","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-arguana.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-arguana-test run.txt Run id 1d28b019480c4407 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-dbpedia-entity \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"cot","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-dbpedia-entity.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-dbpedia-entity-test run.txt Run id 4a6cb049795632d3 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-fiqa \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"cot","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-fiqa.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-fiqa-test run.txt Run id 73ed0aba995f4866 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-scifact \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"cot","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-scifact.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-scifact-test run.txt Run id 6301f4ca76c01f2d · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-covid \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"cot","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-covid.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-covid-test run.txt Run id fb62aa352bc15877 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-news \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"cot","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-news.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-news-test run.txt Run id f3f93eb272b13660 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.dlhard \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"cot","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ /mnt/data/son/Thesis/t5/data/dlhard/neutral_queries.tsv run.txt Run id 67d4fcaa1675df48 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2019 \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"cot","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl19-passage run.txt Run id 73b4aaf48606190b · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2020 \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"cot","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl20-passage run.txt Run id 62c9507b2ac22c16 · open full run detail →
	Q2D (FS)	BM25	0.3984	0.9353	0.3859	0.4831	0.2430	0.5533	0.7149	0.9443	0.7423	0.1668	0.4778	0.5842	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3141	0.7724	0.5884	0.8605	0.5428	0.8691
methodQ2D (FS) llmQwen2.5-7B-Instruct retrieverBM25 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"fs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-arguana.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-arguana-test run.txt Run id 594930d110df8731 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-dbpedia-entity \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"fs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-dbpedia-entity.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-dbpedia-entity-test run.txt Run id 44515c9e869aaff4 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-fiqa \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"fs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-fiqa.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-fiqa-test run.txt Run id 71812da24a9de0e5 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-scifact \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"fs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-scifact.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-scifact-test run.txt Run id d460875ca7114d6c · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-covid \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"fs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-covid.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-covid-test run.txt Run id 78eec3302fce551a · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-news \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"fs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-news.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-news-test run.txt Run id 72e49bd1ea5571d2 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.dlhard \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"fs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ /mnt/data/son/Thesis/t5/data/dlhard/neutral_queries.tsv run.txt Run id 4549e09aba8d1d38 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2019 \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"fs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl19-passage run.txt Run id df47ee08000db77b · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2020 \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"fs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl20-passage run.txt Run id 502160d92b6d3bbd · open full run detail →
	Q2D (ZS)	BM25	0.4007	0.9353	0.3836	0.5047	0.2460	0.5597	0.7042	0.9443	0.7071	0.1628	0.4507	0.5561	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3352	0.7763	0.6014	0.8467	0.5685	0.8647
methodQ2D (ZS) llmQwen2.5-7B-Instruct retrieverBM25 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train","mode":"zs"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-arguana.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-arguana-test run.txt Run id 62e52fd01525fb50 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-dbpedia-entity \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train","mode":"zs"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-dbpedia-entity.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-dbpedia-entity-test run.txt Run id 22d170e3fbbc7a61 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-fiqa \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train","mode":"zs"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-fiqa.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-fiqa-test run.txt Run id 2ebb11584acb74e3 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-scifact \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train","mode":"zs"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-scifact.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-scifact-test run.txt Run id 71bb53177909776f · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-covid \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train","mode":"zs"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-covid.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-covid-test run.txt Run id facc5bc340b19269 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-news \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train","mode":"zs"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-news.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-news-test run.txt Run id 5f13bb51f6d902d5 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.dlhard \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train","mode":"zs"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ /mnt/data/son/Thesis/t5/data/dlhard/neutral_queries.tsv run.txt Run id 03f7128ac3b310e8 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2019 \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"zs"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl19-passage run.txt Run id e923c68752d5194e · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2020 \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train","mode":"zs"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl20-passage run.txt Run id 10225b870ee90958 · open full run detail →
	Q2D (COT)	SPLADE++	0.5200	0.9808	0.3697	0.5223	0.3206	0.6505	0.6825	0.9467	0.6567	0.1147	0.3831	0.4524	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.2731	0.8239	0.6513	0.9037	0.5948	0.9019
methodQ2D (COT) llmQwen2.5-7B-Instruct retrieverSPLADE++ 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"cot","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-arguana.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-arguana-test run.txt Run id 6fc0eb4976b64f71 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-dbpedia-entity \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"cot","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-dbpedia-entity.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-dbpedia-entity-test run.txt Run id 8e1ae4b47cc703f1 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-fiqa \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"cot","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-fiqa.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-fiqa-test run.txt Run id 1ed323f21524e47a · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-scifact \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"cot","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-scifact.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-scifact-test run.txt Run id 78db671246340f4a · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-covid \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"cot","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-covid.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-covid-test run.txt Run id 1c4a65da2e28111a · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-news \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"cot","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-news.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-news-test run.txt Run id 0a9125d7fce9507b · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.dlhard \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"cot","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ /mnt/data/son/Thesis/t5/data/dlhard/neutral_queries.tsv run.txt Run id db5867912a6cff30 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2019 \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"cot","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl19-passage run.txt Run id 3038d21e605c99c3 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2020 \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"cot","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl20-passage run.txt Run id 81ff403926fbda15 · open full run detail →
	Q2D (ZS)	SPLADE++	0.5196	0.9815	0.3531	0.4926	0.3117	0.6509	0.6803	0.9567	0.6673	0.1124	0.4027	0.4812	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.2904	0.8006	0.6091	0.8665	0.6096	0.9045
methodQ2D (ZS) llmQwen2.5-7B-Instruct retrieverSPLADE++ 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"zs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-arguana.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-arguana-test run.txt Run id 10750a2c759310d5 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-dbpedia-entity \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"zs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-dbpedia-entity.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-dbpedia-entity-test run.txt Run id 2a4aea18fc844873 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-fiqa \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"zs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-fiqa.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-fiqa-test run.txt Run id a2160aebb99d75bf · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-scifact \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"zs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-scifact.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-scifact-test run.txt Run id 6b0354dd1f57f0fb · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-covid \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"zs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-covid.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-covid-test run.txt Run id 436633fd6e61404f · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-news \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"zs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-news.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-news-test run.txt Run id 83e36bbdcd01c854 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.dlhard \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train","mode":"zs"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ /mnt/data/son/Thesis/t5/data/dlhard/neutral_queries.tsv run.txt Run id 4be12e26d83028c1 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2019 \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train","mode":"zs"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl19-passage run.txt Run id db822818b660f509 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2020 \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train","mode":"zs"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl20-passage run.txt Run id b0a7a76ed835dd55 · open full run detail →
	Q2D (FS)	SPLADE++	0.5199	0.9808	0.3575	0.4927	0.3079	0.6483	0.7120	0.9500	0.6793	0.1095	0.4146	0.4917	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.2672	0.8116	0.6095	0.8612	0.5492	0.9062
methodQ2D (FS) llmQwen2.5-7B-Instruct retrieverSPLADE++ 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"fs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-arguana.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-arguana-test run.txt Run id e5853bd4e50372e7 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-dbpedia-entity \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"fs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-dbpedia-entity.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-dbpedia-entity-test run.txt Run id 6781ed547701eeb3 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-fiqa \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"fs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-fiqa.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-fiqa-test run.txt Run id 659853e299ae7307 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-scifact \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"fs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-scifact.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-scifact-test run.txt Run id 0d861d7c7a402734 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-covid \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"fs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-covid.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-covid-test run.txt Run id 2b9c9a7ef6f17d79 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-news \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"fs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-news.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-news-test run.txt Run id 320f704b7fecc186 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.dlhard \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"fs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ /mnt/data/son/Thesis/t5/data/dlhard/neutral_queries.tsv run.txt Run id 7a85d826f607f964 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2019 \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"fs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl19-passage run.txt Run id 3ce9480abb45ec9c · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2020 \ --method query2doc \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"fs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl20-passage run.txt Run id 57c4d760aeeba257 · open full run detail →
	query2e	BGE-base-en-v1.5	0.6205	0.9900	0.3415	0.4534	0.3795	0.7132	0.7378	0.9633	0.7618	0.1379	0.4454	0.4967	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3521	0.8171	0.6646	0.8422	0.6425	0.8443
methodquery2e llmQwen2.5-7B-Instruct retrieverBGE-base-en-v1.5 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method query2e \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-arguana.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-arguana-test run.txt Run id cafe249a6ccb78b7 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-dbpedia-entity \ --method query2e \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-dbpedia-entity.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-dbpedia-entity-test run.txt Run id b24982411534c902 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-fiqa \ --method query2e \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-fiqa.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-fiqa-test run.txt Run id 226e448baff0a569 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-scifact \ --method query2e \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-scifact.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-scifact-test run.txt Run id bc803be0dc8b0a33 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-covid \ --method query2e \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-covid.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-covid-test run.txt Run id 65b9e3a50d5b2be4 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-news \ --method query2e \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-news.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-news-test run.txt Run id 88cca24afe966023 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.dlhard \ --method query2e \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ /mnt/data/son/Thesis/t5/data/dlhard/neutral_queries.tsv run.txt Run id 0c47f30fff5b996e · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2019 \ --method query2e \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl19-passage run.txt Run id 0987a90506b8ed6b · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2020 \ --method query2e \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BGE-base-en-v1.5 (dense) python -m pyserini.search.faiss \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.bge-base-en-v1.5 \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder BAAI/bge-base-en-v1.5 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl20-passage run.txt Run id 0201eefb780f030e · open full run detail →
	query2e	BM25	0.4052	0.9403	0.3477	0.4691	0.2453	0.5494	0.6967	0.9520	0.6945	0.1653	0.4503	0.5824	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3101	0.7432	0.5721	0.8431	0.5404	0.8548
methodquery2e llmQwen2.5-7B-Instruct retrieverBM25 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method query2e \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-arguana.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-arguana-test run.txt Run id af1560c7b48c9a40 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-dbpedia-entity \ --method query2e \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-dbpedia-entity.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-dbpedia-entity-test run.txt Run id 36919c88ea5e5218 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-fiqa \ --method query2e \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-fiqa.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-fiqa-test run.txt Run id 13827910963f743c · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-scifact \ --method query2e \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-scifact.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-scifact-test run.txt Run id 08ca78b716915e59 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-covid \ --method query2e \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-covid.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-covid-test run.txt Run id 7be63943b9134828 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-news \ --method query2e \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-news.flat \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-news-test run.txt Run id 9a103d83631d9cae · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.dlhard \ --method query2e \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ /mnt/data/son/Thesis/t5/data/dlhard/neutral_queries.tsv run.txt Run id efc5a614cfca2157 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2019 \ --method query2e \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"mode":"zs","num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl19-passage run.txt Run id 1e142ccfe450e0ea · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2020 \ --method query2e \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · BM25 (lexical) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --bm25 --k1 0.9 --b 0.4 \ --output run.txt \ --hits 1000 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl20-passage run.txt Run id 0a4272e5995a875c · open full run detail →
	query2e	SPLADE++	0.5193	0.9815	0.3386	0.5134	0.2912	0.6256	0.7115	0.9493	0.6080	0.1093	0.4073	0.4888	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3056	0.7882	0.5474	0.8734	0.5312	0.9001
methodquery2e llmQwen2.5-7B-Instruct retrieverSPLADE++ 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method query2e \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-arguana.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-arguana-test run.txt Run id 5a5070df3c8219ba · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-dbpedia-entity \ --method query2e \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-dbpedia-entity.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-dbpedia-entity-test run.txt Run id 8660ffe293b039ea · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-fiqa \ --method query2e \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-fiqa.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-fiqa-test run.txt Run id f687bca9ceff1815 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-scifact \ --method query2e \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-scifact.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-scifact-test run.txt Run id ff1e7a1c379a91d9 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-covid \ --method query2e \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-covid.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-covid-test run.txt Run id 5cbf1afc48359dee · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-trec-news \ --method query2e \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index beir-v1.0.0-trec-news.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@100 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \ beir-v1.0.0-trec-news-test run.txt Run id 2583e6668c775333 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.dlhard \ --method query2e \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ /mnt/data/son/Thesis/t5/data/dlhard/neutral_queries.tsv run.txt Run id 92c57b2d152bec43 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2019 \ --method query2e \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl19-passage run.txt Run id 3b099faf31aa36d5 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2020 \ --method query2e \ --model Qwen/Qwen2.5-7B-Instruct \ --steps reformulate \ --temperature 1 \ --max-tokens 128 \ --method-params '{"num_examples":4,"train_split":"train"}' \ --output-dir outputs/reproduce 2 retrieve pyserini · SPLADE++ (learned_sparse) python -m pyserini.search.lucene \ --threads 16 --batch-size 128 \ --index msmarco-v1-passage.splade-pp-ed \ --topics outputs/reproduce/queries/reformulated_queries.tsv \ --encoder naver/splade-cocondenser-ensembledistil \ --output run.txt \ --hits 1000 --impact 3 evaluate trec_eval · nDCG@10 + R@1k python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.1000 \ dl20-passage run.txt Run id c4fbf354c7648c0e · open full run detail →