PrLM: Learning Explicit Reasoning for Personalized RAG via Contrastive Reward Optimization
This addresses the issue of misaligned responses in personalized RAG for users, though it appears incremental as it builds on existing RAG methods by adding explicit reasoning.
The paper tackles the problem of personalized retrieval-augmented generation (RAG) by proposing PrLM, a reinforcement learning framework that trains large language models to explicitly reason over retrieved user profiles, resulting in improved performance and robustness across datasets and retrieval conditions.
Personalized retrieval-augmented generation (RAG) aims to produce user-tailored responses by incorporating retrieved user profiles alongside the input query. Existing methods primarily focus on improving retrieval and rely on large language models (LLMs) to implicitly integrate the retrieved context with the query. However, such models are often sensitive to retrieval quality and may generate responses that are misaligned with user preferences. To address this limitation, we propose PrLM, a reinforcement learning framework that trains LLMs to explicitly reason over retrieved user profiles. Guided by a contrastively trained personalization reward model, PrLM effectively learns from user responses without requiring annotated reasoning paths. Experiments on three personalized text generation datasets show that PrLM outperforms existing methods and remains robust across varying numbers of retrieved profiles and different retrievers.