Clustered Self-Assessment: A Simple yet Effective Method for Uncertainty Quantification in Large Language Models

ArXi:2606.03846v1 Announce Type: cross Large language models (LLMs) nstrate remarkable performance across diverse tasks, but they often generate responses that appear plausible while being factually incorrect. This problem is compounded by the lack of explicit uncertainty estimates, which makes it difficult for users to judge the reliability of model outputs. Existing uncertainty quantification methods typically rely on indirect signals, such as entropy across sampled generations.