Datasets:
File size: 2,852 Bytes
71e01c9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | ---
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
**Paper**: [MIHBench: Can Multi-modal Large Language Models Understand Multi-Image Inputs?](https://arxiv.org/abs/2505.xxxxx) | ACM Multimedia 2025
## Overview
MIHBench is a comprehensive benchmark for evaluating multi-image understanding and hallucination in Multi-modal Large Language Models (MLLMs). It contains **3,200 samples** across **4 tasks** (800 samples each), with each sample containing 2-4 images.
## Tasks
| Task | # Images | Description |
|------|----------|-------------|
| **Count** | 2 | Determine whether the same number of a target object appears in both images. 400 samples include injected distracting objects. |
| **Existence (Adversarial)** | 3 | Determine whether a target object exists in all images, with adversarially selected objects (rare, confusing). |
| **Existence (Popular)** | 3 | Determine whether a target object exists in all images, using commonly known objects. |
| **Existence (Random)** | 3 | Determine whether a target object exists in all images, using randomly selected objects. |
> **Note**: A 5th task (ID Consistency) will be added in a future update.
## Dataset Schema
### Common columns (all tasks)
| Column | Type | Description |
|--------|------|-------------|
| `images` | `list[image]` | 2-4 images (PIL Image objects) |
| `question` | `str` | Natural language question about the images |
| `label` | `str` | Ground truth answer (`"yes"` or `"no"`) |
| `task` | `str` | Task identifier |
| `num_images` | `int` | Number of images in the sample |
| `image_names` | `list[str]` | Source image filenames |
### Additional columns (Count task only)
| Column | Type | Description |
|--------|------|-------------|
| `injected` | `bool` | Whether distracting objects were injected into the question |
| `object_counts` | `str` | JSON string mapping image identifiers to object counts (e.g., `'{"A": 1, "B": 1}'`) |
## Data Splits
Each task is a separate configuration/split with 800 samples (400 `"yes"`, 400 `"no"`).
## Image Sources
- **Tasks 1-4**: COCO (Common Objects in Context) dataset
- **Task 5** (ID Consistency, coming soon): CO3D dataset
## Citation
If you use MIHBench in your research, please cite:
```bibtex
@inproceedings{mihbench2025,
title={MIHBench: Can Multi-modal Large Language Models Understand Multi-Image Inputs?},
author={},
booktitle={Proceedings of the ACM Multimedia 2025},
year={2025}
}
```
## License
This dataset is released under the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) license. The underlying images are from COCO and CO3D, which have their own licenses.
|