qa_expand

All results produced by QueryGym · fully reproducible!

12 model × retriever configurations for this method 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.

	Model	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
	Model	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
	Qwen2.5-72B-Instruct	BGE-base-en-v1.5	0.6213	0.9900	0.4013	0.4955	0.3891	0.7274	0.7431	0.9667	0.7775	0.1370	0.4842	0.4983	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3485	0.8498	0.6999	0.8733	0.6916	0.8785
methodqa_expand llmQwen2.5-72B-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/Qwen2.5-72B-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 d75dc3435b61737d · 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/Qwen2.5-72B-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 d84f9458a62e7059 · 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/Qwen2.5-72B-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 72847fa3e0c288e0 · 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/Qwen2.5-72B-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 a1c684c668f6fa70 · 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/Qwen2.5-72B-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 63233688412cfd44 · 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/Qwen2.5-72B-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 44dcf54abf2cc8c7 · 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/Qwen2.5-72B-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 7364ebdc2ba6093d · 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/Qwen2.5-72B-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 e4ba6fa7944f9595 · 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/Qwen2.5-72B-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 2e243e54153ec180 · open full run detail →
	Qwen2.5-72B-Instruct	BM25	0.3995	—	0.3744	0.4709	0.2484	—	0.7015	—	0.6809	0.1600	0.4474	0.5517	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3215	0.7876	0.6109	0.8396	0.6152	0.8727
methodqa_expand llmQwen2.5-72B-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/Qwen2.5-72B-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 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 \ beir-v1.0.0-arguana-test run.txt Run id 596c110a56d70dfe · 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/Qwen2.5-72B-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 66a97127eca1d555 · 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/Qwen2.5-72B-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 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 \ beir-v1.0.0-fiqa-test run.txt Run id 96c937c633ca0517 · 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/Qwen2.5-72B-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 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 \ beir-v1.0.0-scifact-test run.txt Run id b21dd96bd1694da4 · 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/Qwen2.5-72B-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 107505689e2b41f0 · 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/Qwen2.5-72B-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 33c770ec0c64ff7b · 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/Qwen2.5-72B-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 2157cb85743005b6 · 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/Qwen2.5-72B-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 7ac8cd9fc5333fcc · 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/Qwen2.5-72B-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 1e81de4a38d3f0dd · open full run detail →
	Qwen2.5-72B-Instruct	SPLADE++	0.5174	0.9794	0.3830	0.5213	0.3333	0.6464	0.6796	0.9393	0.6324	0.1079	0.4168	0.4803	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3347	0.8285	0.6757	0.9005	0.6983	0.9284
methodqa_expand llmQwen2.5-72B-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/Qwen2.5-72B-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 158f4d081331a778 · 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/Qwen2.5-72B-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 97e1f030ff0d3699 · 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/Qwen2.5-72B-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 75929acc64d173b8 · 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/Qwen2.5-72B-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 4476b77e7af895c9 · 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/Qwen2.5-72B-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 47b49e007d29cc1b · 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/Qwen2.5-72B-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 2af9d9ccf528d1d9 · 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/Qwen2.5-72B-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 326588da5cddb65c · 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/Qwen2.5-72B-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 f862bc4b68efd409 · 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/Qwen2.5-72B-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 438bf85c0461670d · open full run detail →
	Qwen2.5-7B-Instruct	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 →
	Qwen2.5-7B-Instruct	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 →
	Qwen2.5-7B-Instruct	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 →
	gpt-4.1	BGE-base-en-v1.5	0.6231	0.9900	0.4005	0.5087	0.4162	0.7452	0.7367	0.9600	0.7954	0.1419	0.4697	0.4852	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3739	0.8543	0.7370	0.8936	0.7074	0.8754
methodqa_expand llmgpt-4.1 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 openai/gpt-4.1 \ --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 d26c77ffed599fc9 · 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 openai/gpt-4.1 \ --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 61fdcf06b8ed12f8 · 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 openai/gpt-4.1 \ --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 d0d48ab44677b302 · 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 openai/gpt-4.1 \ --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 7d23e782850f29f6 · 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 openai/gpt-4.1 \ --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 6c6f3db340322603 · 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 openai/gpt-4.1 \ --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 02cc77e6f1bb633e · 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 openai/gpt-4.1 \ --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 dd84e47122f29fcc · 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 openai/gpt-4.1 \ --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 5d2e065776f6d314 · 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 openai/gpt-4.1 \ --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 cb09157e0e91d145 · open full run detail →
	gpt-4.1	BM25	0.3970	0.9324	0.3699	0.4890	0.2643	0.5814	0.7063	0.9403	0.7065	0.1620	0.4502	0.5608	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3018	0.7570	0.6832	0.8495	0.6418	0.8787
methodqa_expand llmgpt-4.1 retrieverBM25 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method qa_expand \ --model openai/gpt-4.1 \ --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 9b0ab95f82cea1f3 · 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 openai/gpt-4.1 \ --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 d493b98f57ef2733 · 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 openai/gpt-4.1 \ --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 03710d063184324a · 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 openai/gpt-4.1 \ --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 386a7242a957e139 · 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 openai/gpt-4.1 \ --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 6de5557d87e4b4ed · 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 openai/gpt-4.1 \ --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 0e53f287d05ca6e1 · 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 openai/gpt-4.1 \ --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 7f12baf7cb0f69b7 · 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 openai/gpt-4.1 \ --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 e67ec513ea055429 · 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 openai/gpt-4.1 \ --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 300a1ea4d70fb95f · open full run detail →
	gpt-4.1	SPLADE++	0.3823	0.9801	0.3873	0.5289	0.3399	0.6821	0.6964	0.9493	0.6941	0.1152	0.4266	0.4566	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3552	0.8034	0.7335	0.9170	0.6739	0.9260
methodqa_expand llmgpt-4.1 retrieverSPLADE++ 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method qa_expand \ --model openai/gpt-4.1 \ --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 b928c008fc588900 · 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 openai/gpt-4.1 \ --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 ecdcae2a6a8e4eb4 · 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 openai/gpt-4.1 \ --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 51feec618fc5f94f · 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 openai/gpt-4.1 \ --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 f43ee895ee17928d · 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 openai/gpt-4.1 \ --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 2a6b854819defbe0 · 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 openai/gpt-4.1 \ --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 2b47882554623fb1 · 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 openai/gpt-4.1 \ --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 c4fe891fb956c8d1 · 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 openai/gpt-4.1 \ --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 982ea8c8e99ce5ea · 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 openai/gpt-4.1 \ --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 dde02b83d639d757 · open full run detail →
	gpt-4.1-nano	BGE-base-en-v1.5	0.6213	0.9893	0.3718	0.4717	0.3940	0.7272	0.7486	0.9593	0.7489	0.1355	0.4271	0.4749	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3688	0.8113	0.6523	0.8486	0.6612	0.8397
methodqa_expand llmgpt-4.1-nano 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 openai/gpt-4.1-nano \ --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 0b9c2905ff387807 · 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 openai/gpt-4.1-nano \ --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 7242296d30c8c6f3 · 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 openai/gpt-4.1-nano \ --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 e4401a8464968002 · 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 openai/gpt-4.1-nano \ --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 e7202673a4dfdb4c · 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 openai/gpt-4.1-nano \ --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 2d4cd2d96a2ffba7 · 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 openai/gpt-4.1-nano \ --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 5d934e74a7c4506c · 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 openai/gpt-4.1-nano \ --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 d003f3efb99e54af · 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 openai/gpt-4.1-nano \ --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 893a7713d2f59107 · 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 openai/gpt-4.1-nano \ --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 fb40284d5d1aaaa2 · open full run detail →
	gpt-4.1-nano	BM25	0.4021	0.9367	0.3680	0.4808	0.2509	0.5744	0.7059	0.9430	0.6885	0.1583	0.4326	0.5487	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3469	0.7480	0.5819	0.8385	0.6026	0.8649
methodqa_expand llmgpt-4.1-nano retrieverBM25 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method qa_expand \ --model openai/gpt-4.1-nano \ --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 1a833fc972988aa9 · 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 openai/gpt-4.1-nano \ --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 9c31ae911f3ac93f · 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 openai/gpt-4.1-nano \ --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 4c2fff0ca759a5b4 · 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 openai/gpt-4.1-nano \ --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 71bd093e88d1661d · 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 openai/gpt-4.1-nano \ --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 fac4b7355a15d357 · 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 openai/gpt-4.1-nano \ --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 5e2d2c9781d0eb64 · 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 openai/gpt-4.1-nano \ --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 5e03acfddafb7f3a · 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 openai/gpt-4.1-nano \ --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 3e8b7438d8bcac2a · 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 openai/gpt-4.1-nano \ --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 cc360a2a4d13cbe4 · open full run detail →
	gpt-4.1-nano	SPLADE++	0.3811	0.9787	0.4019	0.5396	0.3360	0.6669	0.6939	0.9420	0.7079	0.1215	0.4227	0.4696	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3702	0.8506	0.6883	0.9010	0.6628	0.9279
methodqa_expand llmgpt-4.1-nano retrieverSPLADE++ 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method qa_expand \ --model openai/gpt-4.1-nano \ --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 940d265efc31581a · 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 openai/gpt-4.1-nano \ --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 bc69f76b684cccf3 · 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 openai/gpt-4.1-nano \ --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 9544e547f56b6e35 · 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 openai/gpt-4.1-nano \ --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 4e0076e1e487df2b · 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 openai/gpt-4.1-nano \ --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 949162b32988b17e · 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 openai/gpt-4.1-nano \ --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 58f6b4cbe43aa672 · 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 openai/gpt-4.1-nano \ --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 ba7b3397999a8c70 · 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 openai/gpt-4.1-nano \ --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 d7bf0033504f2795 · 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 openai/gpt-4.1-nano \ --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 741f8800fc2e11f3 · open full run detail →