用多智能体对话界面提升探索任务的个性化支持
Navigating the Unknown: A Chat-Based Collaborative Interface for Personalized Exploratory Tasks
- 构建三面板交互系统，通过多智能体协作识别显性和隐性需求
- 22人实验显示用户更偏好该系统，认知负担降低且创意激发更强
- 适合需要个性化探索与问题引导的科研、创作类用户
大型语言模型（LLMs）的兴起改变了用户与知识系统交互的方式，使聊天机器人能够整合海量信息并协助完成复杂的探索性任务。然而，基于LLM的聊天机器人在用户提出模糊查询或缺乏足够上下文时，往往难以提供个性化支持。本文提出协作式个性化探索助手CARE，结合多智能体LLM框架与结构化用户界面，包含聊天面板、解决方案面板和需求面板，支持迭代查询优化与动态方案生成。多智能体协同识别用户的显性和隐性需求，输出定制化可执行方案。在包含22名参与者的被试内研究中，CARE持续优于基线LLM聊天机器人，用户称赞其能降低认知负荷、激发创造力并提供更贴合需求的解决方案。研究结果表明，CARE有潜力将基于LLM的系统从被动信息检索工具转变为个性化的主动问题解决伙伴。
原文摘要 · Abstract (English)
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.
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