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 interacted 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 interviewed by CLUE. Our experiments demonstrate that CLUE captures interesting user opinions, for example, the bipolar views on the displayed reasoning process of DeepSeek-R1 and demands for information freshness and multi-modality. Our collected chat-and-interview logs will be released.
View on arXiv@article{liu2025_2502.15226,
title={ Understand User Opinions of Large Language Models via LLM-Powered In-the-Moment User Experience Interviews },
author={ Mengqiao Liu and Tevin Wang and Cassandra A. Cohen and Sarah Li and Chenyan Xiong },
journal={arXiv preprint arXiv:2502.15226},
year={ 2025 }
}