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README.md

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 ---
 license: apache-2.0
+task_categories:
+- visual-question-answering
+- image-text-to-text
+language:
+- en
+tags:
+- multimodal
+- benchmark
+- vision-language
+- mllm
+- counter-intuitive
+- commonsense-reasoning
+- language-bias
+size_categories:
+- n<1K
+pretty_name: 'CAIT: Counter-intuitive Action Image Test'
 ---
+
+# 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.
+
+The benchmark accompanies the paper *Seeing vs. Believing: Evaluating the Language
+Bias of Open-Source MLLMs in Counter-Intuitive Scenes*. Humans reach about 0.95
+accuracy on it and leading proprietary models up to about 0.88, while standard
+open-source instruction-tuned models perform at chance level.
+
+This is an **evaluation-only** benchmark; it is published as a single `test` split
+and is not intended for training.
+
+## Loading
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("LukeLing/CAIT", split="test")
+print(ds)
+print(ds[0]["question"], ds[0]["option_a"], ds[0]["option_b"], ds[0]["answer"])
+ds[0]["image"]  # a PIL image
+```
+
+## Fields
+
+| Field | Type | Description |
+| --- | --- | --- |
+| `image` | image | The scene, resolved by the Hub from `file_name` |
+| `id` | string | Four-digit item id, e.g. `0001` |
+| `question` | string | Fixed question stem used for every item during evaluation |
+| `option_a` | string | First answer option |
+| `option_b` | string | Second answer option |
+| `answer` | string | The correct option letter, `A` or `B` |
+| `answer_text` | string | The text of the correct option, so the label survives any reordering |
+| `main_category` | string | Interaction-pattern code: `H-H`, `H-A`, `A-A`, `B-S` |
+| `main_category_name` | string | Readable form: `Human-Human`, `Human-Animal`, `Animal-Animal`, `Bio-StillLife` |
+| `subcategory` | string | One of the six counter-intuitive types below |
+| `gt_prompt` | string | The text-to-image prompt the scene was synthesised from |
+
+The `answer` letters are balanced exactly 200 / 200 across the benchmark, so a
+model that always answers with the same letter scores 0.50.
+
+### The prompt used in the paper
+
+```
+Which of the following option better describes the image?
+Option A: {option_a}
+Option B: {option_b}
+
+Important Note: Answer directly with the option letter (A or B) only.
+```
+
+### About `gt_prompt`
+
+Because every scene is synthetic, the prompt that generated it is the most
+faithful available description of the depicted action. The paper feeds this text
+to a judge model instead of an image to establish an upper bound of about 0.98
+accuracy, which demonstrates that the questions are logically easy once the scene
+is described correctly, and that failures therefore originate in perception.
+
+Note that `gt_prompt` reveals the answer. It is included for reproducibility of
+that upper-bound experiment and should not be shown to a model under test.
+
+## Taxonomy
+
+| Interaction pattern | Counter-intuitive type | Items | Share |
+| --- | --- | --- | --- |
+| Human-Human (`H-H`) | Role Reversal - Social Power | 88 | 22.00% |
+| Human-Human (`H-H`) | Role Reversal - Kinship Care | 57 | 14.25% |
+| Human-Animal (`H-A`) | Animal Dominance over Humans | 89 | 22.25% |
+| Human-Animal (`H-A`) | Animals Providing Humanlike Care | 56 | 14.00% |
+| Animal-Animal (`A-A`) | Prey Outsmarts Predator | 72 | 18.00% |
+| Bio-StillLife (`B-S`) | Objects Act on Beings | 38 | 9.50% |
+| **Total** | | **400** | **100%** |
+
+## Position-bias control
+
+The paper also evaluates a variant in which the two options are presented in the
+opposite order, to separate genuine understanding from a preference for a
+particular letter. That variant is not shipped as a separate file because it is
+fully reconstructible: swap `option_a` with `option_b` and flip `answer`.
+
+```python
+def swap_options(example):
+    example["option_a"], example["option_b"] = example["option_b"], example["option_a"]
+    example["answer"] = "B" if example["answer"] == "A" else "A"
+    return example
+
+swapped = ds.map(swap_options)
+```
+
+## Notes on the images
+
+All 400 files are PNG. 362 are 1024x1024 and the remaining 38 are 512x512.
+237 files carry an alpha channel, but it is fully opaque in every one of them, so
+converting to RGB loses nothing:
+
+```python
+ds = ds.map(lambda x: {"image": x["image"].convert("RGB")})
+```
+
+## Citation
+
+```bibtex
+@inproceedings{ling2026seeing,
+  title     = {Seeing vs. Believing: Evaluating the Language Bias of Open-Source
+               MLLMs in Counter-Intuitive Scenes},
+  author    = {Ling, Chen and Zhang, Tongwei and Li, Hanqian and Ding, Nai},
+  booktitle = {Proceedings of the 2026 Conference on Empirical Methods in Natural
+               Language Processing (EMNLP)},
+  year      = {2026}
+}
+```