让对话系统适当放慢节奏，提升用户思考与系统理解。
Better Slow than Sorry: Introducing Positive Friction for Reliable Dialogue Systems
- 在关键节点故意放慢对话，通过提问或暂停引发用户反思。
- 实验显示任务成功率提升，系统对用户意图的理解更准确。
- 适合追求可靠性和可解释性的对话系统研发者参考。
尽管话语理论与认知科学长期认可缓慢节奏的价值，但当前对话研究倾向于消除对话中的摩擦。然而，无摩擦的对话可能导致用户对AI输出不加批判地依赖，掩盖隐含假设并引发意外后果。为此，我们提出在对话AI中引入正向摩擦机制，旨在促进用户对目标的反思、对系统回应的批判性思考，并推动AI系统的再调整。我们假设，通过在关键时刻有意识地放慢对话速度，主动提问、揭示假设或暂停，可改善目标对齐、用户心理状态建模和任务成功率。本文构建了正向摩擦的本体论，并在多领域及具身化目标导向语料库上收集专家人工标注数据。基于这些语料和使用先进模型的模拟交互实验表明，引入摩擦不仅促进负责任的决策，还增强了机器对用户信念与目标的理解，并显著提升任务成功率。
原文摘要 · Abstract (English)
While theories of discourse and cognitive science have long recognized the value of unhurried pacing, recent dialogue research tends to minimize friction in conversational systems. Yet, frictionless dialogue risks fostering uncritical reliance on AI outputs, which can obscure implicit assumptions and lead to unintended consequences. To meet this challenge, we propose integrating positive friction into conversational AI, which promotes user reflection on goals, critical thinking on system response, and subsequent re-conditioning of AI systems. We hypothesize systems can improve goal alignment, modeling of user mental states, and task success by deliberately slowing down conversations in strategic moments to ask questions, reveal assumptions, or pause. We present an ontology of positive friction and collect expert human annotations on multi-domain and embodied goal-oriented corpora. Experiments on these corpora, along with simulated interactions using state-of-the-art systems, suggest incorporating friction not only fosters accountable decision-making, but also enhances machine understanding of user beliefs and goals, and increases task success rates.
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