Dissecting Training-Free Uncertainty Estimation in Multimodal Large Language Models
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Computer Science > Computation and Language
Title: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.
| Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.22206 [cs.CL] |
| (or arXiv:2609.22206v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.22206
arXiv-issued DOI via DataCite (pending registration)
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