---
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}
}
```