通过用户-助手模拟开发前瞻性与个性化的人工智能助手
ProPerSim: Developing Proactive and Personalized AI Assistants through User-Assistant Simulation
AI总结：
ProPerSim通过用户-助手模拟框架开发了能够主动和个性化推荐的AI助手，实验显示其在多样化的用户场景中有效提升了用户满意度。
摘要：
随着大型语言模型（LLMs）越来越多地融入日常生活，人们对能够不仅被动响应，还能主动和个性化的人工智能助手的需求日益增长。尽管最近的进展推动了主动性和个性化的发展，但两者的结合仍处于探索阶段。为弥合这一差距，我们引入了ProPerSim，一种新的任务和模拟框架，用于开发能够在现实家庭场景中做出及时、个性化的推荐的助手。在我们的模拟环境中，一个具有丰富个性的用户代理与助手互动，对每个建议与其偏好和情境的契合度进行评分。助手的目标是利用这些评分来学习和适应，以在长期内获得更高的分数。基于ProPerSim，我们提出了ProPerAssistant，一种检索增强、偏好对齐的助手，通过用户反馈不断学习和适应。在32种多样化的个性中进行的实验表明，ProPerAssistant能够调整其策略并稳步提高用户满意度，突显了将主动性和个性化结合的潜力。
英文摘要：
As large language models (LLMs) become increasingly integrated into daily life, there is growing demand for AI assistants that are not only reactive but also proactive and personalized. While recent advances have pushed forward proactivity and personalization individually, their combination remains underexplored. To bridge this gap, we introduce ProPerSim, a new task and simulation framework for developing assistants capable of making timely, personalized recommendations in realistic home scenarios. In our simulation environment, a user agent with a rich persona interacts with the assistant, providing ratings on how well each suggestion aligns with its preferences and context. The assistant's goal is to use these ratings to learn and adapt to achieve higher scores over time. Built on ProPerSim, we propose ProPerAssistant, a retrieval-augmented, preference-aligned assistant that continually learns and adapts through user feedback. Experiments across 32 diverse personas show that ProPerAssistant adapts its strategy and steadily improves user satisfaction, highlighting the promise of uniting proactivity and personalization.
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。
