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README.md

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-license: cc-by-4.0
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+---
+license: cc-by-4.0
+---
+
+# MIBench
+
+This dataset is from our EMNLP'24 (main conference) paper [MIBench: Evaluating Multimodal Large Language Models over Multiple Images](https://arxiv.org/abs/2407.15272)
+
+## Introduction
+
+<div align="center">
+    <img src="overview.webp" alt="Overview" style="width: 500px; height: auto;">
+</div>
+
+**MIBench** covers 13 sub-tasks in three typical multi-image scenarios: Multi-Image Instruction, Multimodal Knowledge-Seeking and Multimodal In-Context Learning.
+
+- **Multi-Image Instruction**: This scenario includes instructions for perception, comparison and reasoning across multiple input images. According to the semantic types of the instructions, it is divided into five sub-tasks: General Comparison, Subtle Difference, Visual Referring, Temporal Reasoning and Logical Reasoning.
+
+- **Multimodal Knowledge-Seeking**: This scenario examines the ability of MLLMs to acquire relevant information from external knowledge, which is provided in an interleaved image-text format. Based on the forms of external knowledge, we categorize this scenario into four sub-tasks: Fine-grained Visual Recognition, Text-Rich Images VQA, Vision-linked Textual Knowledge and Text-linked Visual Knowledge.
+
+- **Multimodal In-Context Learning**: In-context learning is another popular scenario, in which MLLMs respond to visual questions while being provided with a series of multimodal demonstrations. To evaluate the model’s MIC ability in a fine-grained manner, we categorize the MIC scenario into four distinct tasks: Close-ended VQA, Open-ended VQA, Hallucination and Demo-based Task Learning.
+
+## Examples
+
+The following image shows the examples of the multi-image scenarios with a total of 13 sub-tasks. The correct answers are marked in blue.
+![](example.webp)
+
+## Data format
+Below shows an example of the dataset format. The `<image>` in the `question` field indicates the location of the images. Note that to ensure better reproducibility, for the Multimodal In-Context Learning scenario, we store the context information of different shots in the `context` field.
+
+```
+{
+    "id": "general_comparison_1",
+    "image": [
+        "image/general_comparison/test1-902-0-img0.png",
+        "image/general_comparison/test1-902-0-img1.png"
+    ],
+    "question": "Left image is <image>. Right image is <image>. Question: Is the subsequent sentence an accurate portrayal of the two images? One lemon is cut in half and has both halves facing outward.",
+    "options": [
+        "Yes",
+        "No"
+    ],
+    "answer": "Yes",
+    "task": "general_comparison",
+    "type": "multiple-choice",
+    "context": null
+},
+```
+
+
+## Citation
+If you find this dataset useful for your work, please consider citing our paper:
+```
+@article{liu2024mibench,
+  title={Mibench: Evaluating multimodal large language models over multiple images},
+  author={Liu, Haowei and Zhang, Xi and Xu, Haiyang and Shi, Yaya and Jiang, Chaoya and Yan, Ming and Zhang, Ji and Huang, Fei and Yuan, Chunfeng and Li, Bing and others},
+  journal={arXiv preprint arXiv:2407.15272},
+  year={2024}
+}
+```