Learning from Natural Language Feedback for Personalized Question Answering

• URL: http://arxiv.org/abs/2508.10695v1
• Date: Thu, 14 Aug 2025 14:36:53 GMT
• Title: Learning from Natural Language Feedback for Personalized Question Answering
• Authors: Alireza Salemi, Hamed Zamani,
• Abstract summary: Personalization is crucial for enhancing the effectiveness and user satisfaction of language technologies.<n>Current approaches for personalizing large language models (LLMs) often rely on retrieval-augmented generation (RAG)<n>We introduce Vac, a novel framework for personalized response generation that replaces scalar rewards with natural language feedback (NLF)
• Score: 21.115495457454365
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Personalization is crucial for enhancing both the effectiveness and user satisfaction of language technologies, particularly in information-seeking tasks like question answering. Current approaches for personalizing large language models (LLMs) often rely on retrieval-augmented generation (RAG), followed by reinforcement learning with scalar reward signals to teach models how to use retrieved personal context. We believe that these scalar rewards sometimes provide weak, non-instructive feedback, limiting learning efficiency and personalization quality. We introduce VAC, a novel framework for personalized response generation that replaces scalar rewards with natural language feedback (NLF) that are generated conditioned on the user profiles and the question narratives. NLF serves as a rich and actionable supervision signal, allowing the policy model to iteratively refine its outputs and internalize effective personalization strategies. Training alternates between optimizing the feedback model and fine-tuning the policy model on the improved responses, resulting in a policy model that no longer requires feedback at inference. Evaluation on the LaMP-QA benchmark that consists of three diverse domains demonstrates consistent and significant improvements over the state-of-the-art results. Human evaluations further confirm the superior quality of the generated responses. These results demonstrate that NLF provides more effective signals for optimizing personalized question answering.

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