IAP uses RL to train LLMs to explicitly infer and apply implicit user intent in single-turn personalized QA, achieving ~7.5% average macro-score gains over baselines on LaMP-QA.
C onv GQR : Generative Query Reformulation for Conversational Search
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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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Training LLMs with Reinforcement Learning for Intent-Aware Personalized Question Answering
IAP uses RL to train LLMs to explicitly infer and apply implicit user intent in single-turn personalized QA, achieving ~7.5% average macro-score gains over baselines on LaMP-QA.
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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.