RCEM aligns conversations to LLM-rewritten queries while preserving the base embedder's space, eliminating label requirements and improving robustness under distributional shift by up to 30%.
Open-Domain Question Answering Goes Conversational via Question Rewriting
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CL 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
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.
A multi-turn RAG system combines learned sparse retrieval with LLM-conditioned rewriting, listwise reranking, and generation to handle conversational QA and unanswerable queries across four domains.
citing papers explorer
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RCEM: Robust Conversational Search EMbedder in Distributional Shift
RCEM aligns conversations to LLM-rewritten queries while preserving the base embedder's space, eliminating label requirements and improving robustness under distributional shift by up to 30%.
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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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uva-irlab-conv at SemEval-2026 Task 8: Multi-Turn RAG with Learned Sparse Retrieval and Listwise Reranking
A multi-turn RAG system combines learned sparse retrieval with LLM-conditioned rewriting, listwise reranking, and generation to handle conversational QA and unanswerable queries across four domains.