Evaluating the Relevance of Uncertainty Estimators for LLM Hallucination 事件

Evaluating the Relevance of Uncertainty Estimators for LLM Hallucination arXiv:2605.27016v1 Announce Type: new Abstract: Large language models (LLMs) are prone to hallucinations, i.e., statements unsupported by the input or training data, hindering reliable deployment. In parallel, numerous uncertainty estimation (UE) methods have been proposed to quantify model confidence and are often implicitly treated as proxies for model failure. However, the relationship between uncertainty and hallucinat