Dataset · Hugging Face

LukeLing/CAIT

Name: CAIT
Creator: LukeLing
License: apache-2.0
Keywords: task_categories:visual-question-answering, task_categories:image-text-to-text, language:en, license:apache-2.0, size_categories:n<1K, format:imagefolder, modality:image, modality:text, library:datasets, library:mlcroissant, region:us, multimodal, benchmark, vision-language, mllm

CAIT: Counter-intuitive Action Image Test CAIT is an evaluation benchmark of 400 high-fidelity synthetic scenes in which the visual evidence deliberately contradicts everyday common sense, for example "a rabbit is chasing a tiger". Each item is a binary forced-choice question between the scene that is actually depicted and the commonsense-consistent scene that is not. Models that lean on language priors instead of looking at the image pick the plausible-sounding option and fail.… See the full description on the dataset page: https://huggingface.co/datasets/LukeLing/CAIT.

View on Hugging FaceReport an issue

Author / organization	LukeLing
License (as declared)	apache-2.0
Downloads	199 (as reported by Hugging Face)
Likes	1
Created	24 August 2026
Last updated at source	25 August 2026

multimodalbenchmarkvision-languagemllmcounter-intuitivecommonsense-reasoninglanguage-bias

Embed this entry

<iframe src="https://silytics.si/embed/item/edef6790-180c-451e-9a56-f16324a64b9b" width="420" height="190" style="border:0" loading="lazy" title="Silytics entry"></iframe>

Free, no tracking. More about feeds and embeds.

Silytics shows public metadata from Hugging Face and links to the original. Information may be incomplete or out of date. Last refreshed 16 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.