BracketRank reranks documents via LLM-driven bracket-style competitive elimination with mandatory reasoning explanations, reaching 26.56 nDCG@10 on BRIGHT and outperforming RankGPT-4 and Rank-R1-14B.
Pyserini: An easy-to-use python toolkit to support replicable IR research with sparse and dense representations.CoRR, abs/2102.10073, 2021
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SWE-Pro benchmark shows LLMs deliver negligible runtime gains and almost no memory reductions on 102 real tasks where experts achieve 15.5x aggregate speedup and 171.3x peak memory reduction.
SPECTRA generates reproducible synthetic IR corpora up to 60,000 documents with controllable distractors, long-tail vocabulary, and graded relevance labels via a single-process Python prototype.
BiCon-Gate improves dialogue fact-checking by applying staged de-colloquialisation and gating rewrites based on semantic consistency with context, yielding gains on the DialFact benchmark over baselines including LLM rewrites.
Mask-to-Correct and M2C+ use diversity-aware masking in RAG to identify erroneous claim spans and produce faithful corrections, outperforming baselines by up to 14% SARI without gold evidence.
citing papers explorer
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BracketRank: Large Language Model Document Ranking via Reasoning-based Competitive Elimination
BracketRank reranks documents via LLM-driven bracket-style competitive elimination with mandatory reasoning explanations, reaching 26.56 nDCG@10 on BRIGHT and outperforming RankGPT-4 and Rank-R1-14B.
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Evaluating LLMs on Real-World Software Performance Optimization
SWE-Pro benchmark shows LLMs deliver negligible runtime gains and almost no memory reductions on 102 real tasks where experts achieve 15.5x aggregate speedup and 171.3x peak memory reduction.
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SPECTRA: Synthetic IR Test Collections with Relevance Oracles and Controlled Distractor Diagnostics
SPECTRA generates reproducible synthetic IR corpora up to 60,000 documents with controllable distractors, long-tail vocabulary, and graded relevance labels via a single-process Python prototype.
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BiCon-Gate: Consistency-Gated De-colloquialisation for Dialogue Fact-Checking
BiCon-Gate improves dialogue fact-checking by applying staged de-colloquialisation and gating rewrites based on semantic consistency with context, yielding gains on the DialFact benchmark over baselines including LLM rewrites.
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Mask-to-Correct$^+$: Leveraging Retriever Diversity for Masking-guided Faithful Fact Correction
Mask-to-Correct and M2C+ use diversity-aware masking in RAG to identify erroneous claim spans and produce faithful corrections, outperforming baselines by up to 14% SARI without gold evidence.