Skip to content
星际流动

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models

发布
采集
学术前沿 6.0 分 — 有一定参考价值的AI研究论文
原文: cs.CL updates on arXiv.org

评分 6.0 · 来源:cs.CL updates on arXiv.org · 发布于 2026-04-08

评分依据:有一定参考价值的AI研究论文

arXiv:2509.09438v2 Announce Type: replace Abstract: Assessing the reliability of Large Language Models (LLMs) by confidence elicitation is a prominent approach to AI safety in high-stakes applications, such as healthcare and finance. Existing methods either require expensive computational overhead or suffer from poor calibration, making them impractical and unreliable for real-world deployment. In this work, we propose GrACE, a Generative Approach to Confidence Elicitation that enables scalable and reliable confidence elicitation for LLMs. GrACE adopts a novel mechanism in which the model expr


标签: