Dissecting Training-Free Uncertainty Estimation in Multimodal Large Language Models

Abstract:Multimodal Large Language Models (MLLMs) have achieved remarkable performance across a wide range of multimodal tasks, yet understanding and quantifying their predictive uncertainty remains underexplored despite being central for safety critical applications. In this work, we present a systematic study of training-free uncertainty quantification strategies for MLLMs, categorizing existing approaches into three conceptual families: token-level methods, which operate directly in the text output space; verbalized methods, which elicit uncertainty estimates or abstention signals via natural language prompts; and semantic methods, which measure uncertainty in a semantic meaning space. We benchmark these strategies across multiple datasets, model families, generations, and scales, and find that no single family dominates: token-level entropy (at sampling temperature 1.0) wins on short answers, verbalized abstention on sentence-length responses, and semantic methods on long-form generation.

Computation and Language (cs.CL); Machine Learning (cs.LG)

arXiv:2609.22206 [cs.CL]

(or arXiv:2609.22206v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.22206 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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