It's Not What the Image Shows: Irrelevant Context Destabilises VLM Judges Without Informing Them
Summary
论文提出 MIST(Misleading Image Stress Test)基准,测试 13 个 VLM judge 在存在对齐、误导或缺失图像时标签的稳定性,发现图像是否出现而非图像内容才是改变判断的主要因素,说明可替代性结论对评测配置同样敏感。
View Cached Full Text
Cached at: 10/01/26, 12:24 PM
Paper page - It’s Not What the Image Shows: Irrelevant Context Destabilises VLM Judges Without Informing Them
Source: https://huggingface.co/papers/2609.37863
Abstract
Vision-languagemodels(VLMs)areincreasinglyusedinplaceofhumanannotators,makingitimportantthatsubstitutabilitytestsreflectthemodelratherthanincidentalevaluationconditions.WeintroduceMIST,theMisleading-ImageStressTest:200Englishsentences,eachbuiltaroundaphrasereadableeitherfigurativelyorliterallyandshownwithanalignedimagedepictingitsreading,amisleadingimagedepictingtheopposite,ornoimageatall.Theguidelinesrequirethelabeltobedecidedfromthesentencealone,sonoimageshouldchangeanyanswer.Weexpectedeachimagetopullajudge’slabelstowardthesenseitdepicts,andneitherkinddid.AcrossthirteenVLMjudges,analignedimagechanged20.5%oflabelsandamisleadingone19.4%,closeforeveryjudgeandbothabovethe11.6%producedbydeletingtheignore-the-imageinstructionwiththeimageleftinplace.Yetonly37%ofthelabelsthatdifferbetweenthetwoimagesmovedtowardthesenseshown,andagreementwithourhumanannotatorsisunchangedwhethertheimageisabsent,alignedormisleading.Theeffectissmallerinthesevenjudgesthatpassthealt-testthaninthesixthatneverdo,butpresentinallofthem:whatmovesajudgeisthatanimageisthere,notwhichofthetwoitis,soasubstitutabilityverdictdescribesaconfigurationasmuchasamodel.
View arXiv pageView PDFAdd to collection
Get this paper in your agent:
hf papers read 2609\.37863
Don’t have the latest CLI?curl \-LsSf https://hf\.co/cli/install\.sh \| bash
Models citing this paper0
No model linking this paper
Cite arxiv.org/abs/2609.37863 in a model README.md to link it from this page.
Datasets citing this paper0
No dataset linking this paper
Cite arxiv.org/abs/2609.37863 in a dataset README.md to link it from this page.
Spaces citing this paper0
No Space linking this paper
Cite arxiv.org/abs/2609.37863 in a Space README.md to link it from this page.
Collections including this paper0
No Collection including this paper
Add this paper to acollectionto link it from this page.
Similar Articles
VLMs can score well on benchmarks, while silently erasing meaningful terms and including hallucinate bias [P]
This paper highlights that VLMs for chest x-ray report generation can score well on benchmarks while erasing clinically meaningful terms and introducing biased language, and proposes a framework to measure these failures.
Frozen Scenes, Shifting Winners: Configuration Fragility in Text-to-3D Evaluation
This paper audits configuration fragility in text-to-3D evaluation by holding 300 generated scenes fixed while varying render and caption settings, finding that configuration variance often exceeds between-generator variance and that VLM-based evaluators frequently shift their reported winner without any change in the underlying scenes.
A Plausible Explanation Is Enough to Mislead Multimodal Judges
该论文提出了一个名为JUPE的视觉问答基准,发现普通的看似合理的解释就足以误导14个多模态大模型评判者,使错误答案的拒绝率下降约12.7-13.8个百分点,研究者将此现象称为“plausibility capture”。
MM-JudgeBias: A Benchmark for Evaluating Compositional Biases in MLLM-as-a-Judge
Researchers introduce MM-JudgeBias, a benchmark that exposes systematic compositional biases in multimodal large language models when used as automatic judges, testing 26 SOTA MLLMs across 1,800 samples.
When Noise Fabricates Bias: The Fragility of LLM-as-a-Judge Bias Measurement under Noisy Text
This paper investigates how surface noise in text affects LLM judges' bias measurement, finding that it systematically overestimates bias, particularly in fairness-critical categories, and introduces the Fable benchmark to study this issue.