Navigating the Unknown: A Chat-Based Collaborative Interface for
  Personalized Exploratory Tasks
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.
672470d2b690f2c92a2c9183
Yingzhe Peng
claimed_verified
2024-11-05T16:09:27.196Z
64e82a99ad1bac272b73b5ab
https://cdn-avatars.huggingface.co/v1/production/uploads/64e82a99ad1bac272b73b5ab/AL2bPCy5rrGxG1YrzJ76Z.jpeg
YingzhePeng
user
ColeYzzzz
672470d2b690f2c92a2c9184
Xiaoting Qin
672470d2b690f2c92a2c9185
Zhiyang Zhang
672470d2b690f2c92a2c9186
Jue Zhang
672470d2b690f2c92a2c9187
Qingwei Lin
672470d2b690f2c92a2c9188
Xu Yang
672470d2b690f2c92a2c9189
Dongmei Zhang
672470d2b690f2c92a2c918a
Saravan Rajmohan
672470d2b690f2c92a2c918b
Qi Zhang
Navigating the Unknown: A Chat-Based Collaborative Interface for
  Personalized Exploratory Tasks
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.
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.
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