User Feedback in Human-LLM Dialogues: A Lens to Understand Users But Noisy as a Learning Signal

• URL: http://arxiv.org/abs/2507.23158v1
• Date: Wed, 30 Jul 2025 23:33:29 GMT
• Title: User Feedback in Human-LLM Dialogues: A Lens to Understand Users But Noisy as a Learning Signal
• Authors: Yuhan Liu, Michael J. Q. Zhang, Eunsol Choi,
• Abstract summary: We study implicit user feedback in two user-LM interaction datasets.<n>We find that the contents of user feedback can improve model performance in short human-designed questions.<n>We also find that the usefulness of user feedback is largely tied to the quality of the user's initial prompt.
• Score: 58.43749783815486
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Once language models (LMs) are deployed, they can interact with users long-term, ideally evolving continuously based on their feedback. Asking for direct user feedback can be disruptive; thus, we study harvesting user feedback from user-LM interaction logs. We study implicit user feedback in two user-LM interaction datasets (WildChat and LMSYS). First, we analyze user feedback in the user-LLM conversation trajectory, providing insights into when and why such feedback occurs. Second, we study harvesting learning signals from such implicit user feedback. We find that the contents of user feedback (e.g., user wanted clarification), not just the polarity (e.g., users were unhappy with the previous model response), can improve model performance in short human-designed questions (MTBench) but not on longer and more complex questions (WildBench). We also find that the usefulness of user feedback is largely tied to the quality of the user's initial prompt. Together, we provide an in-depth study of implicit user feedback, showing its potential and limitations.

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