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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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.IR 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
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.
QPP methods can select query variants that boost end-to-end RAG quality over the original query, though retrieval-optimized variants often fail to produce the best generated answers, revealing a utility gap.
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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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.
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Can QPP Choose the Right Query Variant? Evaluating Query Variant Selection for RAG Pipelines
QPP methods can select query variants that boost end-to-end RAG quality over the original query, though retrieval-optimized variants often fail to produce the best generated answers, revealing a utility gap.