Datasets:
metadata
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? | 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:
@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 license. The underlying images are from COCO and CO3D, which have their own licenses.