LLM confidence miscalibrated in hidden-information tasks, study finds

A new research paper explores the disconnect between Large Language Models' (LLMs) stated confidence and their actual accuracy in decision-making, particularly in scenarios with hidden information. The study found that LLMs often express high confidence in their predictions even when those predictions are incorrect, a phenomenon observed across multiple model configurations and providers. This miscalibration can go undetected by standard evaluations that focus solely on outcomes, highlighting a critical gap in assessing LLM reliability for agentic systems. AI

影响 Highlights a critical gap in LLM reliability for agentic systems, suggesting current evaluations may not capture true decision-making quality.

排序理由 Research paper published on arXiv detailing LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]

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