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[2602.19317] Learning to Reason for Multi-Step Retrieval of Personal Context in Personalized Question Answering
Summary
The paper presents PR2, a novel framework for personalized question answering that enhances multi-step reasoning by integrating user-specific contexts, outperforming existing methods.
Why It Matters
Personalization in question answering is crucial for improving user satisfaction and engagement. This research addresses limitations in current methods by introducing a reinforcement learning approach that optimizes retrieval and reasoning processes, potentially transforming how AI systems interact with users.
Key Takeaways
- PR2 framework integrates reasoning and retrieval for personalized QA.
- Outperforms existing methods by 8.8%-12% on the LaMP-QA benchmark.
- Utilizes reinforcement learning to adaptively determine retrieval strategies.
- Focuses on aligning responses with user-specific preferences and context.
- Addresses the limitations of surface-level personalization in QA systems.
Computer Science > Computation and Language arXiv:2602.19317 (cs) [Submitted on 22 Feb 2026] Title:Learning to Reason for Multi-Step Retrieval of Personal Context in Personalized Question Answering Authors:Maryam Amirizaniani, Alireza Salemi, Hamed Zamani View a PDF of the paper titled Learning to Reason for Multi-Step Retrieval of Personal Context in Personalized Question Answering, by Maryam Amirizaniani and 2 other authors View PDF HTML (experimental) Abstract:Personalization in Question Answering (QA) requires answers that are both accurate and aligned with users' background, preferences, and historical context. Existing state-of-the-art methods primarily rely on retrieval-augmented generation (RAG) solutions that construct personal context by retrieving relevant items from the user's profile. Existing methods use the user's query directly to retrieve personal documents, and such strategies often lead to surface-level personalization. We propose PR2 (Personalized Retrieval-Augmented Reasoning), a reinforcement learning framework that integrates reasoning and retrieval from personal context for personalization. PR2 learns adaptive retrieval-reasoning policies, determining when to retrieve, what evidence to retrieve from user profiles, and how to incorporate it into intermediate reasoning steps. By optimizing multi-turn reasoning trajectories under a personalized reward function, the framework reinforces reasoning paths that better align with user-specific preferences an...
