探索未知：面向个性化探索任务的基于聊天的协作界面

Navigating the Unknown: A Chat-Based Collaborative Interface for Personalized Exploratory Tasks

October 31, 2024

作者: Yingzhe Peng, Xiaoting Qin, Zhiyang Zhang, Jue Zhang, Qingwei Lin, Xu Yang, Dongmei Zhang, Saravan Rajmohan, Qi Zhang

cs.AI

摘要

大型语言模型（LLMs）的兴起彻底改变了用户与基于知识的系统的互动方式，使聊天机器人能够综合大量信息并协助处理复杂的探索性任务。然而，基于LLM的聊天机器人在提供个性化支持方面经常遇到困难，特别是当用户提出模糊查询或缺乏足够的上下文信息时。本文介绍了协作式个性化探索助手（CARE），这是一个旨在通过将多智能体LLM框架与结构化用户界面相结合，以增强探索性任务个性化的系统。CARE的界面包括聊天面板、解决方案面板和需求面板，实现了迭代式查询优化和动态解决方案生成。多智能体框架合作识别用户的显性和隐性需求，提供量身定制的可操作解决方案。在一项涉及22名参与者的被试研究中，CARE始终优于基准LLM聊天机器人，用户赞扬其减轻认知负担、激发创造力和提供更贴心解决方案的能力。我们的研究结果突显了CARE将LLM系统从被动信息检索者转变为主动参与个性化问题解决和探索的潜力。

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.