Learning Uncertainty from Sequential Internal Dispersion in Large Language Models

ArXi:2604.15741v1 Announce Type: cross Uncertainty estimation is a promising approach to detect hallucinations in large language models (LLMs). Recent approaches commonly depend on model internal states to estimate uncertainty. However, they suffer from strict assumptions on how hidden states should evolve across layers, and from information loss by solely focusing on last or mean tokens.