AINeutralarXiv – CS AI · 5h ago6/10
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LLMs Uncertainty Quantification via Adaptive Conformal Semantic Entropy
Researchers propose Adaptive Conformal Semantic Entropy (ACSE), a novel method for quantifying uncertainty in large language model outputs by measuring semantic diversity rather than relying solely on lexical or probabilistic measures. The approach uses conformal calibration to provide statistical guarantees on error rates, demonstrating significant performance improvements over existing uncertainty quantification baselines.