Q2D (FS)

query2doc-fs

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	0.6190	0.9900	0.4113	0.5101	0.4098	0.7431	0.7540	0.9633	0.7891	0.1401	0.4857	0.5135	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3845	0.8568	0.7419	0.9027	0.6792	0.8913
methodQ2D (FS) 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 query2doc \ --model Qwen/Qwen2.5-72B-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 51d162995ec0a2f3 · 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/Qwen2.5-72B-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 6e69a7a71d16e72e · 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/Qwen2.5-72B-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 11285804e631bcdb · 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/Qwen2.5-72B-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 01d878ea2fc9b053 · 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/Qwen2.5-72B-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 49b18dfea2539cae · 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/Qwen2.5-72B-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 9518c746ad052cd4 · 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/Qwen2.5-72B-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 01d59a5d7b8d4ab4 · 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/Qwen2.5-72B-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 eb1caa0c2dd69663 · 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/Qwen2.5-72B-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 9e402b046c4e8388 · open full run detail →
	Qwen2.5-72B-Instruct	BM25	0.3991	—	0.3904	0.4993	0.2509	—	0.7163	—	0.7078	0.1673	0.4807	0.6048	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3467	0.8020	0.6875	0.8959	0.6264	0.8907
methodQ2D (FS) llmQwen2.5-72B-Instruct retrieverBM25 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method query2doc \ --model Qwen/Qwen2.5-72B-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 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 \ beir-v1.0.0-arguana-test run.txt Run id a52cee8a07f43fc7 · 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/Qwen2.5-72B-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 26e4b3a7f993e212 · 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/Qwen2.5-72B-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 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 \ beir-v1.0.0-fiqa-test run.txt Run id b8404ff066b9fe20 · 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/Qwen2.5-72B-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 python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 \ beir-v1.0.0-scifact-test run.txt Run id f82daa013c693bfe · 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/Qwen2.5-72B-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 b57adb12adaa79d2 · 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/Qwen2.5-72B-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 c9cdc67d7569d8b2 · 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/Qwen2.5-72B-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 ce90fad5adaa8336 · 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/Qwen2.5-72B-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 7846f4e4f7cbdf8e · 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/Qwen2.5-72B-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 8f3dba10175f7a3a · open full run detail →
	Qwen2.5-72B-Instruct	SPLADE++	0.5200	0.9801	0.3662	0.5023	0.3261	0.6552	0.7035	0.9567	0.6689	0.1142	0.4238	0.4846	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3333	0.8206	0.7151	0.9124	0.6499	0.9234
methodQ2D (FS) llmQwen2.5-72B-Instruct retrieverSPLADE++ 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method query2doc \ --model Qwen/Qwen2.5-72B-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 d4c96038b2421b72 · 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/Qwen2.5-72B-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 1431b663f3476c4a · 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/Qwen2.5-72B-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 3b87caa9466242e1 · 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/Qwen2.5-72B-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 86a415d70ffeb20b · 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/Qwen2.5-72B-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 b2bfdd905ff25123 · 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/Qwen2.5-72B-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 f938ffe59e55de6a · 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/Qwen2.5-72B-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 bfd75142f402414e · 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/Qwen2.5-72B-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 ec284b56cc4b0cee · 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/Qwen2.5-72B-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 22abf0511cc254f7 · open full run detail →
	Qwen2.5-7B-Instruct	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 →
	Qwen2.5-7B-Instruct	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 →
	Qwen2.5-7B-Instruct	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 →
	gpt-4.1	BGE-base-en-v1.5	0.6179	0.9893	0.4302	0.5303	0.4205	0.7542	0.7519	0.9667	0.8039	0.1411	0.4715	0.5157	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.4074	0.8726	0.7272	0.8890	0.7141	0.8948
methodQ2D (FS) 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 query2doc \ --model openai/gpt-4.1 \ --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 8566cd876432b47b · 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 openai/gpt-4.1 \ --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 af761f012a920511 · 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 openai/gpt-4.1 \ --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 0541a2cd6d8ce125 · 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 openai/gpt-4.1 \ --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 5bfc018d1c67bf03 · 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 openai/gpt-4.1 \ --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 5bfc774bed970e40 · 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 openai/gpt-4.1 \ --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 c6b2b52112d9ab4e · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.dlhard \ --method query2doc \ --model openai/gpt-4.1 \ --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 b795dfa531489314 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2019 \ --method query2doc \ --model openai/gpt-4.1 \ --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 3941d1b3c50145f8 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2020 \ --method query2doc \ --model openai/gpt-4.1 \ --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 ca60bfe85fcb687d · open full run detail →
	gpt-4.1	BM25	0.4012	0.9410	0.4010	0.5083	0.2684	0.5993	0.7123	0.9493	0.7081	0.1639	0.4801	0.5842	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3562	0.8042	0.6904	0.8861	0.6746	0.8984
methodQ2D (FS) llmgpt-4.1 retrieverBM25 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method query2doc \ --model openai/gpt-4.1 \ --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 855dea1f38920b07 · 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 openai/gpt-4.1 \ --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 34623e48f90c3cc1 · 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 openai/gpt-4.1 \ --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 6bfad70532b504da · 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 openai/gpt-4.1 \ --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 cc81773cafb3e49b · 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 openai/gpt-4.1 \ --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 cb9b33c3da55c047 · 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 openai/gpt-4.1 \ --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 1cc44d38cba745c8 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.dlhard \ --method query2doc \ --model openai/gpt-4.1 \ --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 d3a0e5118a282df0 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2019 \ --method query2doc \ --model openai/gpt-4.1 \ --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 c995d4ba0c450885 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2020 \ --method query2doc \ --model openai/gpt-4.1 \ --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 46298ea9c2569a93 · open full run detail →
	gpt-4.1	SPLADE++	0.3826	0.9808	0.3910	0.5192	0.3446	0.6890	0.7093	0.9567	0.6591	0.1099	0.4302	0.5009	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3771	0.8396	0.6932	0.9068	0.6749	0.9389
methodQ2D (FS) llmgpt-4.1 retrieverSPLADE++ 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method query2doc \ --model openai/gpt-4.1 \ --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 8794c0d811dd0620 · 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 openai/gpt-4.1 \ --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 88e70d512f12573c · 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 openai/gpt-4.1 \ --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 b5323edbfe15c86b · 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 openai/gpt-4.1 \ --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 d846402d817b7919 · 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 openai/gpt-4.1 \ --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 eea8b97081893f7f · 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 openai/gpt-4.1 \ --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 6f4bbd67016eedf4 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.dlhard \ --method query2doc \ --model openai/gpt-4.1 \ --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 68f40b0c1ca7bb4d · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2019 \ --method query2doc \ --model openai/gpt-4.1 \ --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 29cc3246515308c3 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2020 \ --method query2doc \ --model openai/gpt-4.1 \ --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 f31f40bb5ce4a185 · open full run detail →
	gpt-4.1-nano	BGE-base-en-v1.5	0.6188	0.9900	0.4026	0.5104	0.4039	0.7311	0.7417	0.9567	0.7793	0.1402	0.4539	0.4763	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3480	0.8374	0.7157	0.8601	0.6988	0.8742
methodQ2D (FS) 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 query2doc \ --model openai/gpt-4.1-nano \ --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 5c68574a029be6c6 · 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 openai/gpt-4.1-nano \ --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 860463eac41581f4 · 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 openai/gpt-4.1-nano \ --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 2258115b18436487 · 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 openai/gpt-4.1-nano \ --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 f995abcf4f2bf0ca · 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 openai/gpt-4.1-nano \ --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 2194d748fda6be64 · 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 openai/gpt-4.1-nano \ --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 dbe00db471b41a3c · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.dlhard \ --method query2doc \ --model openai/gpt-4.1-nano \ --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 1bd7027edeaccedc · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2019 \ --method query2doc \ --model openai/gpt-4.1-nano \ --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 4068662f84855fe9 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2020 \ --method query2doc \ --model openai/gpt-4.1-nano \ --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 768832d4f0db3bb8 · open full run detail →
	gpt-4.1-nano	BM25	0.3965	0.9324	0.3720	0.4873	0.2531	0.5833	0.7053	0.9410	0.6827	0.1634	0.4442	0.5398	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3358	0.7627	0.6643	0.8527	0.6227	0.8848
methodQ2D (FS) llmgpt-4.1-nano retrieverBM25 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method query2doc \ --model openai/gpt-4.1-nano \ --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 39a00cbcddc345d8 · 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 openai/gpt-4.1-nano \ --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 bf0024f85cb42407 · 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 openai/gpt-4.1-nano \ --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 f7d84e961ff5f12f · 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 openai/gpt-4.1-nano \ --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 95bd7ea7d41c9761 · 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 openai/gpt-4.1-nano \ --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 6fd7188c2ec94cec · 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 openai/gpt-4.1-nano \ --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 6503b696a29566b0 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.dlhard \ --method query2doc \ --model openai/gpt-4.1-nano \ --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 c832e5deec3b5b50 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2019 \ --method query2doc \ --model openai/gpt-4.1-nano \ --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 47f9b50aa4ac8e38 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2020 \ --method query2doc \ --model openai/gpt-4.1-nano \ --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 d1c162f3189b28c9 · open full run detail →
	gpt-4.1-nano	SPLADE++	0.3823	0.9801	0.3790	0.5204	0.3390	0.6636	0.7121	0.9400	0.6715	0.1182	0.4292	0.4573	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.3533	0.8005	0.6318	0.8839	0.6471	0.9232
methodQ2D (FS) llmgpt-4.1-nano retrieverSPLADE++ 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset beir-v1.0.0-arguana \ --method query2doc \ --model openai/gpt-4.1-nano \ --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 8381004825e16937 · 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 openai/gpt-4.1-nano \ --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 81b43c387e9d258d · 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 openai/gpt-4.1-nano \ --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 666a7b55b1d8b00d · 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 openai/gpt-4.1-nano \ --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 56a8fc44296dddef · 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 openai/gpt-4.1-nano \ --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 001286d1ee758db1 · 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 openai/gpt-4.1-nano \ --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 80f477d3cc16b166 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.dlhard \ --method query2doc \ --model openai/gpt-4.1-nano \ --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 0abb612e9107c2a8 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2019 \ --method query2doc \ --model openai/gpt-4.1-nano \ --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 b84b7426ddae8d52 · open full run detail → 1 reformulate querygym → reformulated_queries.tsv python examples/querygym_pyserini/pipeline.py \ --dataset msmarco-v1-passage.trecdl2020 \ --method query2doc \ --model openai/gpt-4.1-nano \ --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 da4c9162a78e48e2 · open full run detail →