这篇论文提出了CoMet，能高效估计多模态语言模型的不确定性，比现有方法更准更快，很适合做AI可靠性的研究者看。
CoMet是一种针对多模态大语言模型（MLLM）的不确定性估计方法，将不确定性分解为上下文项和多样性项，分别捕捉任务或提示引发的歧义以及上下文确定的合理答案数量。该方法训练一个轻量级后验不确定性模块，无需自回归生成答案或重复采样即可高效估计不确定性。在多个开放多模态基准测试、幻觉检测和多项选择VQA基准上，CoMet持续优于现有基线，且保持计算效率。代码已开源。
CoMet: Context and Multiplicity Decomposition for Multimodal Uncertainty Estimation
Uncertainty estimation has been a long-standing challenge in AI models; it amounts to "knowing what you don't know," and metacognition is notoriously difficult even for humans (cf. the Dunning-Kruger effect). Although it is still far from solved even in simpler classification systems, tackling it in multimodal large language models (MLLMs) is becoming increasingly important. Within MLLMs, uncertainty can stem from any of the diverse sources as well as from their relationships, and further can stem from the unbounded answers in the open-ended setting. To tackle the issues, we propose CoMet, an MLLM uncertainty estimation method by decomposing uncertainty into a context-specific term and a multiplicity-specific term. The former captures ambiguity induced by the given context (e.g., task or prompt), while the latter captures how many plausible answers determined by the context remain compatible with the given input. We train a lightweight post-hoc uncertainty module to estimate these quantities, which enables efficient uncertainty estimation without autoregressive answer generation or repeated sampling. Experiments on various open-ended multimodal benchmarks, hallucination detection, and multiple-choice visual question answering benchmarks show that CoMet consistently improves uncertainty estimation over existing baselines while remaining efficient in practice. Code is available at https://github.com/princetonvisualai/comet_uncertainty