AI RESEARCH
Towards Generation-Efficient Uncertainty Estimation in Large Language Models
arXiv CS.LG
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ArXi:2605.06053v1 Announce Type: new Uncertainty estimation is important for deploying LLMs in high-stakes applications such as healthcare and finance, where hallucinations can appear fluent and plausible while being factually incorrect, making it difficult for users to judge whether an output should be trusted. Existing methods require one or full autoregressive generations to estimate uncertainty, which