title: Navigating the Unknown: A Chat-Based Collaborative Interface for
    Personalized Exploratory Tasks
summary: 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.
paper_authors._id: 672470d2b690f2c92a2c9183
paper_authors.name: Yingzhe Peng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-05T16:09:27.196Z
paper_authors._id: 64e82a99ad1bac272b73b5ab
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64e82a99ad1bac272b73b5ab/AL2bPCy5rrGxG1YrzJ76Z.jpeg
paper_authors.fullname: YingzhePeng
paper_authors.type: user
paper_authors.user: ColeYzzzz
paper_authors._id: 672470d2b690f2c92a2c9184
paper_authors.name: Xiaoting Qin
paper_authors._id: 672470d2b690f2c92a2c9185
paper_authors.name: Zhiyang Zhang
paper_authors._id: 672470d2b690f2c92a2c9186
paper_authors.name: Jue Zhang
paper_authors._id: 672470d2b690f2c92a2c9187
paper_authors.name: Qingwei Lin
paper_authors._id: 672470d2b690f2c92a2c9188
paper_authors.name: Xu Yang
paper_authors._id: 672470d2b690f2c92a2c9189
paper_authors.name: Dongmei Zhang
paper_authors._id: 672470d2b690f2c92a2c918a
paper_authors.name: Saravan Rajmohan
paper_authors._id: 672470d2b690f2c92a2c918b
paper_authors.name: Qi Zhang
paper_title: Navigating the Unknown: A Chat-Based Collaborative Interface for
    Personalized Exploratory Tasks
paper_summary: 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.
paper_ai_summary: CARE, a multi-agent LLM framework with a structured user interface, enhances personalization in exploratory tasks by improving user interaction, reducing cognitive load, and providing tailored solutions.
paper_ai_keywords: large language models (LLMs) multi-agent framework structured user interface Chat Panel Solution Panel Needs Panel iterative query refinement dynamic solution generation explicit and implicit user needs personalized problem-solving exploration