设计驱动反馈：理解并克服会话智能体中的用户反馈障碍

摘要

高质量反馈对于实现有效的人机交互至关重要。它能够弥合认知差距、纠正对话偏离并塑造系统行为，无论是在交互过程中还是在模型开发的整个周期内都发挥着关键作用。然而尽管反馈如此重要，人类对AI的反馈却往往频率低且质量欠佳。这一现实差距促使我们必须批判性地审视人机交互中的反馈机制。为理解并克服阻碍用户提供高质量反馈的挑战，我们开展了两项研究，深入探讨人类与对话智能体之间的反馈动态。通过格莱斯合作原则的视角，我们的形成性研究识别出四大反馈障碍——共同基础、可验证性、沟通效率与信息量——这些障碍阻碍了用户提供高质量反馈。基于这些发现，我们提出三项设计原则，并证明整合了符合这些原则的支架系统的对话智能体能够帮助用户提供更优质的反馈。最后，我们向更广泛的人工智能社区发出行动倡议，呼吁通过提升大语言模型的能力来突破反馈障碍。

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.

设计驱动反馈：理解并克服会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