Feedback by Design: Understanding and Overcoming User Feedback Barriers in Conversational Agents

Nikhil Sharma, Zheng Zhang, Daniel Lee, Namita Krishnan, Guang-Jie Ren, Ziang Xiao, Yunyao Li

Feedback by Design: Understanding and Overcoming User Feedback Barriers in Conversational Agents: 1 upvotes on Hugging Face Daily Papers, #46 of 53 papers on 2026-02-04. Day-by-day upvote history.

High-quality feedback is essential for effective human-AI interaction. It bridges knowledge gaps, corrects digressions, and shapes system behavior; both during interaction and throughout model development. Yet despite its importance, human feedback to AI is often infrequent and low quality. This gap motivates a critical examination of human feedback during interactions with AIs. To understand and overcome the challenges preventing users from giving high-quality feedback, we conducted two studies examining feedback dynamics between humans and conversational agents (CAs). Our formative study, through the lens of Grice's maxims, identified four Feedback Barriers -- Common Ground, Verifiability, Communication, and Informativeness -- that prevent high-quality feedback by users. Building on these findings, we derive three design desiderata and show that systems incorporating scaffolds aligned with these desiderata enabled users to provide higher-quality feedback. Finally, we detail a call for action to the broader AI community for advances in Large Language Models capabilities to overcome Feedback Barriers.

Paper page on Hugging Face · arXiv

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