Repeatedly sampling an LLM and using the entropy of answer frequencies yields conformal prediction sets for multiple-choice questions with empirical miscoverage near the target, and AUROC comparable to logit-based scores.
Hallucination detection in foundation models for decision-making: A flexible definition and review of the state of the art
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Conformal Sets in Multiple-Choice Question Answering under Black-Box Settings with Provable Coverage Guarantees
Repeatedly sampling an LLM and using the entropy of answer frequencies yields conformal prediction sets for multiple-choice questions with empirical miscoverage near the target, and AUROC comparable to logit-based scores.