通过用户个性化配置实现对话中的前瞻性个性化
Towards Proactive Personalization through Profile Customization for Individual Users in Dialogues
AI总结：
本文提出PersonalAgent，通过构建和动态完善用户档案，实现对话中的长期个性化和用户偏好推断，提升对话代理的适应性和连贯性。
摘要：
大型语言模型（LLMs）在交互系统中的部署需要与个体用户的细腻和动态偏好进行深度对齐。当前的对齐技术主要解决普遍的人类价值观或静态的单轮偏好，因此无法解决长期个性化和初始用户冷启动问题。为弥合这一差距，我们提出了PersonalAgent，一种新的以用户为中心的终身代理，旨在持续推断和适应用户偏好。PersonalAgent通过将对话分解为单轮交互来构建并动态完善统一的用户档案，将偏好推断视为一个顺序决策任务。实验表明，PersonalAgent在理想化和嘈杂对话环境中的表现均优于强大的提示基于和策略优化基线，同时保持跨会话偏好的一致性。此外，人类评估证实PersonalAgent在捕捉用户偏好时表现自然且连贯。我们的发现强调了长期个性化对于开发更具包容性和适应性的对话代理的重要性。我们的代码可在此获取。
英文摘要：
The deployment of Large Language Models (LLMs) in interactive systems necessitates a deep alignment with the nuanced and dynamic preferences of individual users. Current alignment techniques predominantly address universal human values or static, single-turn preferences, thereby failing to address the critical needs of long-term personalization and the initial user cold-start problem. To bridge this gap, we propose PersonalAgent, a novel user-centric lifelong agent designed to continuously infer and adapt to user preferences. PersonalAgent constructs and dynamically refines a unified user profile by decomposing dialogues into single-turn interactions, framing preference inference as a sequential decision-making task. Experiments show that PersonalAgent achieves superior performance over strong prompt-based and policy optimization baselines, not only in idealized but also in noisy conversational contexts, while preserving cross-session preference consistency. Furthermore, human evaluation confirms that PersonalAgent excels at capturing user preferences naturally and coherently. Our findings underscore the importance of lifelong personalization for developing more inclusive and adaptive conversational agents. Our code is available here.
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。
