Problem
Research questions and friction points this paper is trying to address.
AI Refusals
User Responses
Cognitive Closure
When AI Says "I Am Unable to Answer": Understanding User Responses to AI Refusals
๐ค AI Summary
็ ็ฉถๆข่ฎจไบ็จๆทๅฏนAIๆ็ปๅ็ญ็ๅๅบ๏ผ้่ฟๆนๅๆ็ป้ข็ใ่งฃ้ไธๅฆๅ็จๆท็่ฎค็ฅ้ญๅ้ๆฑๆฅ่งๅฏ็จๆทๆปกๆๅบฆ็ๅๅใ
๐ Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
refusal-based safeguards
hallucinations in LLMs
need for cognitive closure
balanced refusal strategies
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2024-07-25Citations: 6
