Reinforcement Learning Framework for Intent-Aware Personalized QA
A new reinforcement learning framework called IAP (Intent-Aware Personalization) trains language models to infer implicit user intent from single-turn questions and generate personalized answers. The framework uses a tag-based schema to incorporate inferred intent into the model's reasoning steps, optimizing answer trajectories with reinforcement learning. This approach addresses the limitation of existing methods that rely on multi-turn conversations or rich user profiles, which fail in single-turn settings. IAP aims to bridge the gap by explicitly modeling user intent during the reasoning process, enabling more effective personalized question answering. The research is published on arXiv under identifier 2605.12645.
Key facts
- IAP is a reinforcement learning framework for intent-aware personalization
- It trains models to infer implicit user intent from single-turn questions
- Uses a tag-based schema to incorporate intent into thinking steps
- Optimizes intent-aware answer trajectories with reinforcement learning
- Addresses limitations of multi-turn or profile-based personalization
- Published on arXiv with ID 2605.12645
- Focuses on single-turn settings where user intent must be inferred from minimal input
- Aims to improve personalized question answering in language models
Entities
Institutions
- arXiv