LLMs Uncertainty Quantification via Adaptive Conformal Semantic Entropy
LLMs' overconfidence, particularly when hallucinating, poses a significant challenge for the deployment of the models in safety-critical settings and makes a reliable estimation of uncertainty necessary. Existing approaches for uncertainty quantification typically prioritize lexical or probabilistic measures; however, these techniques often ignore the semantic variance of different responses with similar meaning. In this paper, we propose Adaptive Conformal Semantic Entropy (ACSE), a method for estimating prompt-level uncertainty by adaptively measuring semantic dispersion in LLMs outputs. Our uncertainty scoring function is based on clustering semantic entropy of multiple diverse respons...
| Authors | Hamed Karimi, Vaishali Meyappan, Reza Samavi |
|---|---|
| Primary category | cs.LG |
| Published | 5 May 2026 |
| Last updated at source | 23 May 2026 |
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