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arXiv · 2502.15226

Understand User Opinions of Large Language Models via LLM-Powered In-the-Moment User Experience Interviews

Mengqiao Liu·Tevin Wang·Cassandra A. Cohen·Sarah Li·Chenyan Xiong
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Abstract

Which large language model (LLM) is better? Every evaluation tells a story, but what do users really think about current LLMs? This paper presents CLUE, an LLM-powered interviewer that conducts in-the-moment user experience interviews, right after users interact with LLMs, and automatically gathers insights about user opinions from massive interview logs. We conduct a study with thousands of users to understand user opinions on mainstream LLMs, recruiting users to first chat with a target LLM and then be interviewed by CLUE. Our experiments demonstrate that CLUE captures interesting user opinions, e.g., the bipolar views on the displayed reasoning process of DeepSeek-R1 and demands for information freshness and multi-modality. Our code and data are at https://github.com/cxcscmu/LLM-Interviewer.

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Mengqiao Liu, Tevin Wang, Cassandra A. Cohen, Sarah Li, Chenyan Xiong. 2025-02-21. Understand User Opinions of Large Language Models via LLM-Powered In-the-Moment User Experience Interviews. https://arxiv.org/abs/2502.15226

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