新框架将大语言模型预测风险分解为规范不确定性、随机不确定性和认知不确定性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-04 05:00

研究人员提出了一种新颖的三项分解方法，用于分析大语言模型（LLMs）的预测风险，区分了规范不确定性、残余随机不确定性和认知不确定性。该框架解决了用户无需重新训练模型即可澄清不明确输入的情景，从而提高了预测准确性。所提出的分解有助于识别何时请求澄清是有益的，其中规范不确定性对于检测模糊输入至关重要，而残余随机不确定性则用于识别分布内错误。 AI

影响引入了一个新的理论框架，用于理解和分解大语言模型中的不确定性，有望提高其可靠性和可解释性。

排序理由该条目是一篇在arXiv上发表的学术论文，详细介绍了一个理解大语言模型不确定性的新理论框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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报道来源 [1]

arXiv cs.CL TIER_1 English(EN) · Roman Vashurin, Mikhail Seleznyov, Assel Yermekova, Fedor Chernogorskii, Danila Zykov, Elena Tutubalina, Alexander Panchenko, Preslav Nakov, Nikita Kotelevskii, Maxim Panov · 2026-11-04 05:00

面向规格的预测不确定性分解

arXiv:2611.02956v1 Announce Type: cross Abstract: Classical decompositions of predictive uncertainty, such as the information-theoretic one, are defined for a fixed representation $x$ of the input object. They do not consider that $x$ may be incomplete, nor do they measure how mu…

本文如何被排名

报道来源 [1]

面向规格的预测不确定性分解

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