The rise of large language models (LLMs) has revolutionized user interactions with knowledge-based systems, enabling chatbots to synthesize vast amounts of information and assist with complex, exploratory tasks. However, LLM-based chatbots often struggle to provide personalized support, particularly when users start with vague queries or lack sufficient contextual information. This paper introduces the Collaborative Assistant for Personalized Exploration (CARE), a system designed to enhance personalization in exploratory tasks by combining a multi-agent LLM framework with a structured user interface. CARE's interface consists of a Chat Panel, Solution Panel, and Needs Panel, enabling iterative query refinement and dynamic solution generation. The multi-agent framework collaborates to identify both explicit and implicit user needs, delivering tailored, actionable solutions. In a within-subject user study with 22 participants, CARE was consistently preferred over a baseline LLM chatbot, with users praising its ability to reduce cognitive load, inspire creativity, and provide more tailored solutions. Our findings highlight CARE's potential to transform LLM-based systems from passive information retrievers to proactive partners in personalized problem-solving and exploration.

翻译：大型语言模型（LLM）的兴起彻底改变了用户与知识系统的交互方式，使聊天机器人能够综合海量信息并协助完成复杂的探索性任务。然而，基于LLM的聊天机器人在提供个性化支持方面往往存在不足，特别是当用户以模糊查询开始或缺乏足够上下文信息时。本文介绍了用于个性化探索的协作助手（CARE），该系统通过将多智能体LLM框架与结构化用户界面相结合，旨在增强探索任务中的个性化程度。CARE的界面由聊天面板、解决方案面板和需求面板组成，支持迭代式查询优化与动态解决方案生成。其多智能体框架通过协作识别用户的显性与隐性需求，提供定制化的可执行解决方案。在一项涉及22名参与者的受试者内用户研究中，CARE持续获得比基准LLM聊天机器人更高的偏好度，用户称赞其能够降低认知负荷、激发创造力并提供更具针对性的解决方案。我们的研究结果表明，CARE有潜力将基于LLM的系统从被动的信息检索工具转变为个性化问题解决与探索过程中的主动协作伙伴。