---
dataset_info:
  features:
  - name: images
    sequence: image
  - name: question
    dtype: string
  - name: label
    dtype: string
  - name: task
    dtype: string
  - name: num_images
    dtype: int64
  - name: image_names
    sequence: string
  - name: injected
    dtype: bool
  - name: object_counts
    dtype: string
  configs:
  - config_name: count
    data_files:
    - split: train
      path: data/count.parquet
  - config_name: existence_adversarial
    data_files:
    - split: train
      path: data/existence_adversarial.parquet
  - config_name: existence_popular
    data_files:
    - split: train
      path: data/existence_popular.parquet
  - config_name: existence_random
    data_files:
    - split: train
      path: data/existence_random.parquet
license: cc-by-4.0
task_categories:
- visual-question-answering
language:
- en
tags:
- multi-image
- hallucination
- benchmark
- vision-language-model
- multimodal
size_categories:
- 1K<n<10K
---

# MIHBench

Multi-Image Hallucination Benchmark for evaluating multi-image understanding in MLLMs. 3,200 samples across 4 tasks (800 each), with each sample containing 2-4 images from COCO.

## Fields

| Field | Description |
|-------|-------------|
| images | 2-4 images (list) |
| question | Natural language question about the images |
| label | Ground truth: "yes" or "no" |
| task | Task identifier |
| num_images | Number of images in the sample |
| image_names | Source image filenames |

Additional fields for **count** task: `injected` (bool), `object_counts` (JSON string).

## Tasks

| Task | # Images | Description |
|------|----------|-------------|
| count | 2 | Same number of target object in both images? |
| existence_adversarial | 3 | Target object exists in all images? (rare/confusing objects) |
| existence_popular | 3 | Target object exists in all images? (common objects) |
| existence_random | 3 | Target object exists in all images? (random objects) |

## Evaluation

```
metrics: Accuracy, Precision, Recall, F1
parser: yes/no binary
```

## Source

Original data from [MIHBench](https://arxiv.org/abs/2505.xxxxx) (ACM Multimedia 2025).