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LLM confidence miscalibrated in hidden-information tasks, study finds

By PulseAugur Editorial · [1 sources] ·

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

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

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

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM confidence miscalibrated in hidden-information tasks, study finds

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

  1. arXiv cs.AI TIER_1 English(EN) · Bhushan Kashinath Joshi ·

    Confident at the moment of action: belief miscalibration in LLM play under hidden information

    arXiv:2608.24691v1 Announce Type: new Abstract: Agentic systems increasingly gate actions on a model's own stated confidence, which assumes confidence tracks correctness at the moment of acting. We test this in a hidden-information chess variant where royal status can be secretly…

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