发表机构
The Pennsylvania State University; Seoul National University（宾夕法尼亚州立大学; 首尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过实验考察拒绝频率、解释和认知闭合需求对用户反应的影响，发现拒绝降低满意度但解释可缓解偶尔拒绝的负面效应，揭示了避免幻觉与用户满意度之间的张力。
AI 中文摘要
虽然基于拒绝的防护措施在减少大型语言模型（LLM）中的幻觉方面变得越来越普遍，但这些措施可能与用户对明确答案的偏好相冲突。然而，我们对于用户在重复交互中如何回应拒绝、拒绝何时变得或多或少可接受，以及哪些用户群体对此反应不同，知之甚少。在本研究中，我们考察了拒绝频率、解释以及认知闭合需求（NFCC）如何影响用户对AI拒绝回应的反应。参与者（N=599）与一个从不拒绝、偶尔拒绝或频繁拒绝的AI系统进行交互，其中拒绝行为有的附带解释，有的没有解释。参与者对真实回答的满意度最高，其次是幻觉回答，最后是拒绝回答，尽管他们认识到幻觉回答的准确性较低。解释提高了对偶尔拒绝的满意度，但对频繁拒绝没有这种效果。高NFCC的参与者对频繁拒绝的AI系统评价更为负面。这些发现揭示了避免幻觉与用户满意度之间的张力，并强调了设计平衡拒绝策略的重要性。
英文摘要
While refusal-based safeguards to mitigate hallucinations in large language models (LLMs) are becoming increasingly common, they may conflict with users' preferences for definitive answers. However, we know little about how users respond to refusals across repeated interactions, when refusals become more or less acceptable, and for whom. In this work, we examine how refusal frequency, explanations, and need for cognitive closure (NFCC) shape responses to AI refusals. Participants (N=599) interacted with an AI system that never refused, refused infrequently, or refused frequently, with refusals either explained or unexplained. Participants were most satisfied with genuine responses, followed by hallucinations and then refusals, despite recognizing hallucinations as less accurate. Explanations increased satisfaction with infrequent, but not frequent, refusals. Higher-NFCC participants evaluated AI systems that refused more negatively. These findings reveal a tension between hallucination avoidance and user satisfaction and highlight the importance of designing balanced refusal strategies.
