CHR improves medical question answering retrieval by explicitly promoting evidence aligned with a correct hypothesis while penalizing content aligned with a plausible incorrect alternative.
Corpus-Steered Query Expansion with Large Language Models
3 Pith papers cite this work. Polarity classification is still indexing.
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cs.IR 3years
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UNVERDICTED 3representative citing papers
STORM trains lexical query rewriters via reward-guided beam search that converts retrieval metrics into stepwise token signals, enabling 0.6B-8B models to rival dense retrievers on TREC, BEIR and MIRACL without index changes.
A unified evaluation finds LLM query reformulation gains are strongly conditioned on retrieval paradigm, do not consistently transfer to neural retrievers, and are not uniformly improved by larger LLMs.
citing papers explorer
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Ruling Out to Rule In: Contrastive Hypothesis Retrieval for Medical Question Answering
CHR improves medical question answering retrieval by explicitly promoting evidence aligned with a correct hypothesis while penalizing content aligned with a plausible incorrect alternative.
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STORM: Stepwise Token Optimization with Reward-Guided Beam Search
STORM trains lexical query rewriters via reward-guided beam search that converts retrieval metrics into stepwise token signals, enabling 0.6B-8B models to rival dense retrievers on TREC, BEIR and MIRACL without index changes.
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A Reproducibility Study of LLM-Based Query Reformulation
A unified evaluation finds LLM query reformulation gains are strongly conditioned on retrieval paradigm, do not consistently transfer to neural retrievers, and are not uniformly improved by larger LLMs.