Confidence Calibration: When Should an LLM Say "I Don't Know"? (ANSWERED)
Scenario question on abstention — retrieval scores, calibration, UX of uncertainty, and avoiding confident wrong answers.
TL;DR — Quick Answer
Well-calibrated systems abstain when evidence or model confidence is insufficient — prefer "I don't know" over plausible fiction. Signals: low retrieval similarity, disagreement across samples, high entropy tokens, faithfulness judge failure, out-of-domain classifier. Tune thresholds on golden set optimizing precision over recall for high-stakes domains. UX: explain what was missing, suggest next steps, offer human handoff. miscalibration — overconfident wrong answers — is often worse than silent errors in regulated apps.
The Interview Question
When should an LLM say "I don't know"? Explain confidence calibration and abstention policies in production.
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