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license: cc-by-nc-4.0 |
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# 🧪 Multimodal Benchmark |
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This repository provides a benchmark suite for evaluating Multimodal Large Language Models (MLLMs) across a variety of visual-language tasks. |
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## 📁 Directory Structure |
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### `/data` |
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This folder contains all benchmark images and task-specific JSON files. Each JSON file defines the input and expected output format for a given task. |
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### `/run` |
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This folder includes example scripts that demonstrate how to run different MLLMs on the benchmark tasks. |
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## 📄 Result Collection |
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After inference, all task JSON outputs should be merged into a single file named `result.json`. |
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Each entry in `result.json` includes a `response` field that stores the model's prediction. |
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## 📊 Evaluation |
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The predictions stored in `result.json` can be evaluated using `metric.py`. |
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This script computes performance metrics by comparing the predicted responses with the reference answers. |
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## 💡 Ad Understanding Task |
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The **Ad Understanding** task requires an additional LLM-based preprocessing step before evaluation. |
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An example of deploying a language model for this purpose is provided in [`gpt_judge.py`](./gpt_judge.py). |
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