探索未知：一個基於聊天的協作界面，用於個性化的探索任務

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

大型語言模型（LLMs）的崛起已經徹底改變了使用者與基於知識的系統的互動方式，使聊天機器人能夠綜合龐大的資訊並協助處理複雜的探索任務。然而，基於LLM的聊天機器人在提供個性化支援時常常遇到困難，特別是當使用者提出模糊查詢或缺乏足夠的上下文信息時。本文介紹了協作式個性化探索助手（CARE），這是一個旨在通過結合多智能體LLM框架和結構化使用者界面來增強探索任務個性化的系統。CARE的界面包括聊天面板、解決方案面板和需求面板，使得可以進行迭代式查詢優化和動態解決方案生成。多智能體框架合作識別明確和隱含的使用者需求，提供量身定制的可行解決方案。在一項對22名參與者進行的組內研究中，CARE一直優於基準LLM聊天機器人，使用者讚揚其減輕認知負荷、激發創造力和提供更貼身解決方案的能力。我們的研究結果突顯了CARE將LLM為基礎的系統從被動信息檢索者轉變為主動參與個性化問題解決和探索的潛力。

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.