新方法揭示MLLMs中的认知不确定性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-05 04:00

研究人员开发了一种名为因果不变掩码（Causal-Invariant Masking, CIM）的新方法，以更好地量化多模态大型语言模型（MLLMs）中的认知不确定性。该方法旨在通过区分数据歧义引起的不确定性与模型局限性引起的不确定性来解决MLLMs产生幻觉的问题。提出的语义散度（Semantic Divergence）指标及其更快的代理指标预期嵌入漂移（Expected Embedding Drift, EED）在各种基准测试中均表现出最先进的性能，其中EED提供了显著的速度提升。 AI

影响通过更好地检测幻觉和模型局限性来提高MLLMs的可靠性。

排序理由详细介绍MLLMs中不确定性量化新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Haoyang Luo, Linwei Tao, Jie Gui, Xinghao Chen, Chang Xu, Jianyuan Guo, Minjing Dong · 2026-10-05 04:00

通过因果不变掩码揭示多模态大语言模型中的认知不确定性

arXiv:2610.02887v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) suffer from hallucinations, creating a critical need for Uncertainty Quantification (UQ) to ensure reliable deployment. However, existing approaches struggle to detect uncertainty caused by…

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报道来源 [1]

通过因果不变掩码揭示多模态大语言模型中的认知不确定性

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