#llm-uncertainty News & Analysis

2 articles tagged with #llm-uncertainty. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

2 articles

AIBullisharXiv – CS AI · May 287/10

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Localizing Input Uncertainty Quantification for Large Language Models via Shapley Values

Researchers introduce ShaQ, a Shapley-value-based framework that identifies which specific parts of user input cause uncertainty in large language models, rather than just flagging overall uncertainty. The method achieves state-of-the-art ambiguity detection on multiple benchmarks and demonstrates practical value in high-stakes domains like clinical settings by enabling targeted input clarification.

AINeutralarXiv – CS AI · May 76/10

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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.