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README.md

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+---
+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.