AI RESEARCH
The Persuasion Paradox: When LLM Explanations Fail to Improve Human-AI Team Performance
arXiv CS.AI
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ArXi:2604.03237v1 Announce Type: cross While natural-language explanations from large language models (LLMs) are widely adopted to improve transparency and trust, their impact on objective human-AI team performance remains poorly understood. We identify a Persuasion Paradox: fluent explanations systematically increase user confidence and reliance on AI without reliably improving, and in some cases undermining, task accuracy.