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- K-DTCBench.csv +3 -0
- K-DTCBench.md +95 -0
.gitattributes
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# Video files - compressed
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# Video files - compressed
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*.mp4 filter=lfs diff=lfs merge=lfs -text
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*.webm filter=lfs diff=lfs merge=lfs -text
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K-DTCBench.csv filter=lfs diff=lfs merge=lfs -text
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K-DTCBench.csv
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version https://git-lfs.github.com/spec/v1
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oid sha256:3ee9d974c425105a308d0bbdaa3ee05fe82db372ef69e9185b249deec6f48da1
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size 12886645
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K-DTCBench.md
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---
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license: cc-by-nc-4.0
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---
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# K-DTCBench
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We introduce **K-DTCBench**, a newly developed Korean benchmark featuring both computer-generated and handwritten documents, tables, and charts.
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It consists of 80 questions for each image type and two questions per image, summing up to 240 questions in total.
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This benchmark is designed to evaluate whether vision-language models can process images in different formats and be applicable for diverse domains.
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All images are generated with made-up values and statements for evaluation purposes only. We scanned hand-written documents/tables/charts, or created digital objects with matplotlib library to build K-DTCBench.
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The proportions of digital and hand-written images are equal, each constituting 50%.
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For more details, Please refer to the [VARCO-VISION technical report(Coming Soon)]().
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<table>
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<tr>
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<th>Category</th>
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<th>Image</th>
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<th>K-DTCBench</th>
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</tr>
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<tr>
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<td align="center">document</td>
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<td width=350><img src="https://cdn-uploads.huggingface.co/production/uploads/624ceaa38746b2f5773c2d1c/Ipi4HR73P-PDC5XcgP3WF.png"></td>
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<td>
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<strong>question:</strong> ๋ณด๊ณ ์์ ์ฃผ์ ๋ด์ฉ์ด ์๋ ๊ฒ์ ๋ฌด์์ธ๊ฐ์?
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<br>
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<strong>A:</strong> ์์ ์ธํ๋ผ ํ์ถฉ
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<br>
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<strong>B:</strong> ์ฌ๋ ๋ฐ ์ฌ๊ณ ์๋ฐฉ ์ฒด๊ณ ๊ตฌ์ถ
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<br>
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<strong>C:</strong> ์๋ฏผ ์์ ๊ต์ก ๊ฐํ
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<br>
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<strong>D:</strong> ๊ธด๊ธ ๋์ ์์คํ
๊ฐ์
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</td>
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</tr>
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<tr>
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<td align="center">table</td>
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<td width=350><img src="https://cdn-uploads.huggingface.co/production/uploads/624ceaa38746b2f5773c2d1c/dz_FuPnpZ5P4P3LEB5PZ0.png"></td>
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<td>
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<strong>question:</strong> ์ธํ๋ผ ๊ตฌ์ถ ํญ๋ชฉ์ ์ ์๋ ๋ช ์ ์ธ๊ฐ์?
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<br>
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<strong>A:</strong> 4
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<br>
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<strong>B:</strong> 6
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<br>
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<strong>C:</strong> 8
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<br>
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<strong>D:</strong> 10
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</td>
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</tr>
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<tr>
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<td align="center">chart</td>
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<td width=350><img src="https://cdn-uploads.huggingface.co/production/uploads/624ceaa38746b2f5773c2d1c/IbNMPPgd974SbCAsz6zIS.png"></td>
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<td>
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<strong>question:</strong> ์ง์ฅ์ธ๋ค์ด ํด๊ทผ ํ ๋ ๋ฒ์งธ๋ก ์ ํธํ๋ ํ๋์ ๋ฌด์์ธ๊ฐ์?
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<br>
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<strong>A:</strong> ์ด๋
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<br>
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<strong>B:</strong> ์ฌ๊ฐํ๋
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<br>
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<strong>C:</strong> ์๊ธฐ๊ฐ๋ฐ
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<br>
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<strong>D:</strong> ํด์
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</td>
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</tr>
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</table>
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<br>
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## Inference Prompt
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```
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<image>
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{question}
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Options: A: {A}, B: {B}, C: {C}, D: {D}
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์ฃผ์ด์ง ์ ํ์ง ์ค ํด๋น ์ต์
์ ๋ฌธ์๋ก ์ง์ ๋ตํ์ธ์.
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```
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<br>
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## Results
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Below are the evaluation results of various vision-language models, including [VARCO-VISION-14B]() on K-DTCBench.
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| | VARCO-VISION-14B | Pangea-7B | Pixtral-12B | Molmo-7B-D | Qwen2-VL-7B-Instruct | LLaVA-One-Vision-7B |
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| :---: | :---: | :---: | :---: | :---: | :---: | :---: |
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| K-DTCBench | **84.58** | 48.33 | 27.50 | 45.83 | 75.00 | 52.91 |
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<br>
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## Citation
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(bibtex will be updated soon..) If you use K-DTCBench in your research, please cite the following:
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```
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```
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