Navigating the Unknown: A Chat-Based Collaborative Interface for Personalized Exploratory Tasks
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909373825024000 |
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| author | Peng, Yingzhe Qin, Xiaoting Zhang, Zhiyang Zhang, Jue Lin, Qingwei Yang, Xu Zhang, Dongmei Rajmohan, Saravan Zhang, Qi |
| author_facet | Peng, Yingzhe Qin, Xiaoting Zhang, Zhiyang Zhang, Jue Lin, Qingwei Yang, Xu Zhang, Dongmei Rajmohan, Saravan Zhang, Qi |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_24032 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Navigating the Unknown: A Chat-Based Collaborative Interface for Personalized Exploratory Tasks Peng, Yingzhe Qin, Xiaoting Zhang, Zhiyang Zhang, Jue Lin, Qingwei Yang, Xu Zhang, Dongmei Rajmohan, Saravan Zhang, Qi Human-Computer Interaction Artificial Intelligence Computation and Language 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. |
| title | Navigating the Unknown: A Chat-Based Collaborative Interface for Personalized Exploratory Tasks |
| topic | Human-Computer Interaction Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2410.24032 |