反馈设计：理解并克服对话智能体中的用户反馈障碍

摘要

高質量反饋對於實現有效的人機互動至關重要。這種反饋能夠彌合認知差距、糾正對話偏離軌道，並在互動過程中持續塑造系統行為，同時對模型開發具有長期指導意義。然而儘管其重要性不言而喻，人類對AI的反饋卻往往頻率不足且質量欠佳。這種現實落差促使我們必須對人機互動中的反饋機制進行批判性審視。為理解並突破阻礙用戶提供高質量反饋的挑戰，我們開展了兩項針對人與對話智能體（CAs）反饋動態的研究。通過格萊斯會話準則的理論視角，我們的基礎研究識別出四大反饋障礙——共同基礎、可驗證性、溝通效能與信息密度——這些障礙系統性地制約著用戶的反饋質量。基於這些發現，我們提出三項設計原則，並實證表明：融入符合這些原則的支架式設計的系統，能有效幫助用戶提供更優質的反饋。最後，我們向更廣泛的AI社群發出行動倡議，呼籲通過提升大語言模型的能力來突破反饋障礙。

English

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.

反馈设计：理解并克服对话智能体中的用户反馈障碍

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

摘要

Support