mlsanityData to 14 Oct 2026

Dissecting Training-Free Uncertainty Estimation in Multimodal Large Language Models

arXiv 2609.22206 ↗First posted 1 Sep 2026cs.CL cs.LGcited by 0

On the map: Multimodal LLMs / vision-language models → MLLM training and visual instruction tuning

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 28 of its 43 references that are in the map. First posted on arXiv 1 Sep 2026.

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28 of 43 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024Unveiling Uncertainty: A Deep Dive into Calibration and Performance of Multimodal Large Language Models19 Dec
  2. 2024Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution18 Sep
  3. 2023MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities4 Aug

Other references · 25

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
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  3. 2023Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyondcited by 2,224
  4. 2023MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGIcited by 2,080
  5. 2022Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answeringcited by 1,919
  6. 2022Language Models (Mostly) Know What They Knowcited by 1,856
  7. 2025InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiencycited by 1,510
  8. 2023Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMscited by 974
  9. 2022Teaching Models to Express Their Uncertainty in Wordscited by 751
  10. 2023Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decodingcited by 688
  11. 2024Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similaritiescited by 159
  12. 2024A Comprehensive Survey of Hallucination in Large Language, Image, Video and Audio Foundation Modelscited by 100
13 more
  1. 2025Medical Hallucinations in Foundation Models and Their Impact on Healthcarecited by 93
  2. 2022Reliable Visual Question Answering: Abstain Rather Than Answer Incorrectlycited by 89
  3. 2024VL-Uncertainty: Detecting Hallucination in Large Vision-Language Model via Uncertainty Estimationcited by 73
  4. 2025A Survey of Uncertainty Estimation Methods on Large Language Modelscited by 62
  5. 2025A Survey of Multimodal Hallucination Evaluation and Detectioncited by 27
  6. 2024Atomic Calibration of LLMs in Long-Form Generationscited by 24
  7. 2025Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Modelscited by 16
  8. 2024Enhancing Vision-Language Model Reliability with Uncertainty-Guided Dropout Decodingcited by 15
  9. 2025Object-Level Verbalized Confidence Calibration in Vision-Language Models via Semantic Perturbationcited by 12
  10. 2024The Need for Guardrails with Large Language Models in Medical Safety-Critical Settings: An Artificial Intelligence Application in the Pharmacovigilance Ecosystemcited by 11
  11. 2016The Color of the Cat is Gray: 1 Million Full-Sentences Visual Question Answering (FSVQA)cited by 7
  12. 2025Know What You do Not Know: Verbalized Uncertainty Estimation Robustness on Corrupted Images in Vision-Language Modelscited by 6
  13. 2025Are vision language models robust to uncertain inputs?cited by 3

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