Measuring Epistemic Humility in Multimodal Large Language Models
Hallucinations in multimodal large language models (MLLMs) -- where the model
generates content inconsistent with the input image -- pose significant risks
in real-world applications, from misinformation in visual question answering to
unsafe errors in decision-making. Existing benchmarks primarily test
recognition accuracy, i.e., evaluating whether models can select the correct
answer among distractors. This overlooks an equally critical capability for
trustworthy AI: recognizing when none of the provided options are correct, a
behavior reflecting epistemic humility. We present HumbleBench, a new
hallucination benchmark designed to evaluate MLLMs' ability to reject plausible
but incorrect answers across three hallucination types: object, relation, and
attribute. Built from a panoptic scene graph dataset, we leverage fine-grained
scene graph annotations to extract ground-truth entities and relations, and
prompt GPT-4-Turbo to generate multiple-choice questions, followed by a
rigorous manual filtering process. Each question includes a "None of the above"
option, requiring models not only to recognize correct visual information but
also to identify when no provided answer is valid. We evaluate a variety of
state-of-the-art MLLMs -- including both general-purpose and specialized
reasoning models -- on HumbleBench and share valuable findings and insights
with the community. By incorporating explicit false-option rejection,
HumbleBench fills a key gap in current evaluation suites, providing a more
realistic measure of MLLM reliability in safety-critical settings. Our code and
dataset are released publicly and can be accessed at
https://github.com/maifoundations/HumbleBench.
68c8ccf3733e345e52ac1e73
Bingkui Tong
68c8ccf3733e345e52ac1e74
Jiaer Xia
claimed_verified
2025-09-16T09:42:07.209Z
654c865e17d83697c75f3f90
https://cdn-avatars.huggingface.co/v1/production/uploads/654c865e17d83697c75f3f90/JW79nXWaCKmw2HmELshEw.png
JiaerXia
user
JiaerX
68c8ccf3733e345e52ac1e75
Sifeng Shang
68c8ccf3733e345e52ac1e76
Kaiyang Zhou
Measuring Epistemic Humility in Multimodal Large Language Models
Hallucinations in multimodal large language models (MLLMs) -- where the model
generates content inconsistent with the input image -- pose significant risks
in real-world applications, from misinformation in visual question answering to
unsafe errors in decision-making. Existing benchmarks primarily test
recognition accuracy, i.e., evaluating whether models can select the correct
answer among distractors. This overlooks an equally critical capability for
trustworthy AI: recognizing when none of the provided options are correct, a
behavior reflecting epistemic humility. We present HumbleBench, a new
hallucination benchmark designed to evaluate MLLMs' ability to reject plausible
but incorrect answers across three hallucination types: object, relation, and
attribute. Built from a panoptic scene graph dataset, we leverage fine-grained
scene graph annotations to extract ground-truth entities and relations, and
prompt GPT-4-Turbo to generate multiple-choice questions, followed by a
rigorous manual filtering process. Each question includes a "None of the above"
option, requiring models not only to recognize correct visual information but
also to identify when no provided answer is valid. We evaluate a variety of
state-of-the-art MLLMs -- including both general-purpose and specialized
reasoning models -- on HumbleBench and share valuable findings and insights
with the community. By incorporating explicit false-option rejection,
HumbleBench fills a key gap in current evaluation suites, providing a more
realistic measure of MLLM reliability in safety-critical settings. Our code and
dataset are released publicly and can be accessed at
https://github.com/maifoundations/HumbleBench.
HumbleBench evaluates multimodal large language models' ability to reject incorrect answers, addressing the issue of hallucinations in visual question answering and decision-making.
multimodal large language models hallucinations visual question answering epistemic humility panoptic scene graph dataset fine-grained scene graph annotations multiple-choice questions false-option rejection