在你说话之前了解你：多轮对话中用于LLM个性化的用户状态建模
Know You Before You Speak: User-State Modeling for LLM Personalization in Multi-Turn Conversation
AI总结：
提出基于自由能原理的PUMA框架，通过显式用户状态模型和动作选择机制，将个性化对话从被动记忆检索转变为基于模型的用户演化决策，在医疗咨询基准上提升长期对话效果。
摘要：
个性化对话不仅需要回忆显式的用户历史：系统还需要推断通过交互演化并塑造适当响应策略的隐藏用户状态。现有的基于记忆和配置文件的方法主要重用可观察的用户信息，对建模用户状态动态或基于它们如何塑造未来用户状态来选择动作的支持有限。我们提出了PUMA（面向动作选择的预期用户状态建模），这是一个基于自由能原理（FEP）的框架，将个性化形式化为部分可观测下的决策，围绕一个显式的用户状态模型，该模型捕获潜在用户状态及其动作条件动态。在每一轮中，PUMA维护对用户隐藏状态的信念，细化用于观测生成和动作条件状态转移的用户状态模型，并通过最小化预期自由能来选择对话动作，在统一标准下平衡认知和实用目标。这种表述将个性化从被动记忆检索转变为基于模型的用户演化决策。我们在面向医疗咨询和动机性访谈的基准上实例化PUMA，并带有潜在状态标注以进行严格评估。实验表明，PUMA在保持强响应质量的同时改善了长期对话结果，跨数据集研究展示了更可靠的用户状态估计和下一状态预测。
英文摘要：
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 their action-conditioned dynamics. At each turn, PUMA maintains a belief over the user's hidden state, refines the user state model for observation generation and action-conditioned state transition, and selects dialogue actions by minimizing expected free energy, balancing epistemic and pragmatic objectives under a unified criterion. This formulation shifts personalization from passive memory retrieval to model-based decision-making over user evolution. We instantiate PUMA on healthcare-oriented counseling and motivational interviewing benchmarks with latent state annotations for rigorous evaluation. Experiments show that PUMA improves long-horizon dialogue outcomes while maintaining strong response quality, and a cross-dataset study demonstrates more reliable user-state estimation and next-state prediction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
