LLM Confidence Scores Are Unreliable Without External Calibration

1 reports archived1 independent sourcesupdated 10/21/2026, 12:27:51

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The article argues that asking LLMs to output confidence scores directly is statistically unsound because internal model probabilities do not align with real-world uncertainty. It recommends calibrating agent reliability using external feedback records and historical accuracy data rather than relying on self-reported numerical values.

LATEST/The article argues that asking LLMs to output confidence scores directly is statistically unsound because internal model probabilities do not align with real-world uncertainty. It recommends calibrating agent reliability using external feedback records and historical accuracy data rather than relying on self-reported numerical values.

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Hacker News AIT2·Oct 21, 12:2768 pts
LLM Confidence Scores Are Unreliable Without External Calibration
- Self-reported confidence scores from LLMs are merely text generation outputs lacking statistical calibration.
- Reliable confidence assessment must rely on external record systems, calculating accuracy by comparing predictions against actual historical outcomes.
- Without a mechanism to verify results (human or automated), any confidence value derived from LLM output is invalid and dangerous.
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