New research finds LLMs are dangerously overconfident — even when they know they're guessing
A paper on LLM self-calibration shows models consistently overstate confidence in their outputs, particularly on multi-step reasoning tasks, raising concerns for any system that uses model confidence as a decision signal.
A research finding highlighted by @Moleh1ll is getting attention for its implications in production AI systems: LLMs are consistently poor at self-calibration, expressing high confidence even on tasks where they're likely to fail. The problem is especially acute on multi-step reasoning, where error compounds but expressed confidence doesn't decrease proportionally.
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