ReformIR adaptively prioritizes reformulations and documents with a surrogate model guided by ranker feedback to boost recall while suppressing drift under fixed reranking budgets.
Query2doc: Query Expansion with Large Language Models
7 Pith papers cite this work. Polarity classification is still indexing.
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InvEvolve evolves white-box inventory policies from LLMs with statistical safety guarantees and outperforms classical and deep learning methods on synthetic and real retail data.
CHR improves medical question answering retrieval by explicitly promoting evidence aligned with a correct hypothesis while penalizing content aligned with a plausible incorrect alternative.
Retrieved query variants from logs combined with LLM-augmented generation improve unsupervised QPP accuracy by up to 30% for neural rankers on TREC DL'19 and DL'20.
LLM-generated reference documents enable dynamic ranked list truncation and adaptive batching for listwise reranking, outperforming prior RLT methods and accelerating processing by up to 66% on TREC benchmarks.
MSPA-CQR improves conversational query rewriting by constructing self-consistent preference data across rewriting, retrieval, and response dimensions and training with prefix-guided multi-faceted direct preference optimization, showing effectiveness in both in- and out-of-distribution settings.
citing papers explorer
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When More Reformulations Hurt: Avoiding Drift using Ranker Feedback
ReformIR adaptively prioritizes reformulations and documents with a surrogate model guided by ranker feedback to boost recall while suppressing drift under fixed reranking budgets.
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InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees
InvEvolve evolves white-box inventory policies from LLMs with statistical safety guarantees and outperforms classical and deep learning methods on synthetic and real retail data.
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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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RAQG-QPP: Query Performance Prediction with Retrieved Query Variants and Retrieval Augmented Query Generation
Retrieved query variants from logs combined with LLM-augmented generation improve unsupervised QPP accuracy by up to 30% for neural rankers on TREC DL'19 and DL'20.
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Dynamic Ranked List Truncation for Reranking Pipelines via LLM-generated Reference-Documents
LLM-generated reference documents enable dynamic ranked list truncation and adaptive batching for listwise reranking, outperforming prior RLT methods and accelerating processing by up to 66% on TREC benchmarks.
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Multi-Faceted Self-Consistent Preference Alignment for Query Rewriting in Conversational Search
MSPA-CQR improves conversational query rewriting by constructing self-consistent preference data across rewriting, retrieval, and response dimensions and training with prefix-guided multi-faceted direct preference optimization, showing effectiveness in both in- and out-of-distribution settings.
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