Navigating the Unknown: A Chat-Based Collaborative Interface for Personalized Exploratory Tasks
This addresses the need for more personalized and proactive AI assistants in exploratory tasks, though it is incremental as it builds on existing LLM and interface techniques.
The paper tackles the problem of LLM-based chatbots struggling with personalized support in exploratory tasks by introducing CARE, a system combining a multi-agent LLM framework with a structured interface; in a user study with 22 participants, CARE was consistently preferred over a baseline chatbot for reducing cognitive load and providing tailored solutions.
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.