Measuring Epistemic Humility in Multimodal Large Language Models
summary
The gist
The gist The authors present HumbleBench, a new hallucination benchmark designed to evaluate false-option rejection in multimodal large language models (MLLMs) under a forced-choice multiple-choice
In short
The authors created HumbleBench, a new evaluation benchmark for multimodal models to test 'false-option rejection.' Instead of just checking if a model gets the right answer, it forces models to choose 'None of the above' when no option is correct. This measures epistemic humility—a model's awareness of its knowledge limits—showing that accuracy alone doesn't reflect true robustness.
Key concepts
- HumbleBench
- A benchmark designed to test if multimodal models can correctly reject all provided multiple-choice options when none are visually supported by the input image. It uses a 'None of the above' option in every question to measure abstention behavior.
- False-Option Rejection
- The specific behavior being tested. This is when a model correctly identifies that out of several given choices, none are supported by the visual evidence in an image, and it chooses to abstain rather than making a wrong guess.
- Epistemic Humility
- A concept used to describe the desired behavior. In this context, it means a model showing awareness of its own limitations; instead of confidently providing incorrect information when unsure, it correctly admits uncertainty by selecting 'None of the above'.
Terminology used across episodes
This episode discusses
- Measuring Epistemic Humility in Multimodal Large Language Models · Paper Radio
- Measuring Massive Multitask Language Understanding
- Qwen2.5-VL Technical Report
- DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding
- InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models
- Phi-4 Technical Report
- Gemma 3 Technical Report
- Pixtral 12B
- Building and better understanding vision-language models: insights and future directions
- Ovis: Structural Embedding Alignment for Multimodal Large Language Model
- Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search
- R1-Onevision: Advancing Generalized Multimodal Reasoning through Cross-Modal Formalization
- Visionary-R1: Mitigating Shortcuts in Visual Reasoning with Reinforcement Learning
- LLaVA-CoT: Let Vision Language Models Reason Step-by-Step
- R1-VL: Learning to Reason with Multimodal Large Language Models via Step-wise Group Relative Policy Optimization
- GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning
- Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
- Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning
- Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding
- A Survey on Hallucination in Large Vision-Language Models
- Reefknot: A Comprehensive Benchmark for Relation Hallucination Evaluation, Analysis and Mitigation in Multimodal Large Language Models
The paper
Measuring Epistemic Humility in Multimodal Large Language Models · Read on arXiv
Bingkui Tong, Jiaer Xia, Sifeng Shang, *Kaiyang Zhou
Mohamed bin Zayed University of Artificial Intelligence · Hong Kong Baptist University
Transcript
Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.
Tom: Today's paper: "Measuring Epistemic Humility in Multimodal Large Language Models".
Jane: The gist The authors present HumbleBench,
Tom: First, who's behind it and why it matters.
Paper summary: Tom: So we’ve been diving into HumbleBench, this new benchmark for multimodal models that tests if they just get the right answer or if they know when to admit they don't know.
Lu: The work shows that different model architectures handle this refusal behavior in very different ways, so we can’t just pick one and assume it works everywhere.
Meng: It sounds like the next step for us is figuring out how to build testing pipelines that can catch these subtle failures before they hit our production traffic.
Conclusion: Tom: So we’re wrapping up this paper by Bingkui Tong and his team from MBZUAI and HKBU, titled "Measuring Epistemic Humility in Multimodal Large Language Models."
Jane: That title is pretty heavy, Tom. What does that actually mean for us on the ground?
Tom: It means they aren't just measuring what the model gets right on an image. They are measuring how much the model knows about its own limitations when it looks at a picture.
Lu: They use this specific test, forcing a multiple-choice format with that "None of the above" option, to see if the AI can correctly refuse to guess when no answer fits visually.
Meng: So, if you deploy this stuff, we’re not just looking for high accuracy on standard tasks. We’re looking for models that can admit when they are blind or unsure instead of making a confident mistake.
Lalam: Exactly. This moves the focus away from just picking the right label and toward testing genuine restraint in multimodal reasoning.
Jane: It really highlights that trust in AI isn't just about getting the right answer sometimes. It’s about knowing when to stop guessing altogether when there’s no visual proof for a claim.
Tom: And they found that simply having a high recognition score doesn't guarantee you have this humility, which is why these new metrics are so important for checking if models are actually safe to deploy.
Lu: The work shows that different model architectures handle this refusal behavior in very different ways, so we can’t just pick one and assume it works everywhere.
Meng: It sounds like the next step for us is figuring out how to build testing pipelines that can catch these subtle failures before they hit our production traffic.
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