Know You Before You Speak: User-State Modeling for LLM Personalization in Multi-Turn Conversation
Personalized dialogue requires more than recalling explicit user histories: systems also need to infer hidden user states that evolve through interaction and shape appropriate response strategies. Existing memory- and profile-based methods primarily reuse observable user information, offering limited support for modeling user-state dynamics or selecting actions based on how they shape future user states. We propose PUMA (Prospective User-state Modeling for Action selection), a framework grounded in the Free Energy Principle (FEP) that formulates personalization as decision-making under partial observability, centered on an explicit user state model that captures latent user states and the...
| Authors | Jiani Luo, Xiaoyan Zhao, Yang Zhang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 23 May 2026 |
| Last updated at source | 23 May 2026 |
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