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document_0
λ³΄κ³ μ„œμ˜ μ£Όμš” λ‚΄μš©μ΄ μ•„λ‹Œ 것은 λ¬΄μ—‡μΈκ°€μš”?
μ•ˆμ „ 인프라 ν™•μΆ©
μž¬λ‚œ 및 사고 예방 체계 ꡬ좕
μ‹œλ―Ό μ•ˆμ „ ꡐ윑 κ°•ν™”
κΈ΄κΈ‰ λŒ€μ‘ μ‹œμŠ€ν…œ κ°œμ„ 
B
document
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D
document
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TI-2023-001
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IT-2024-001
C
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document
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document_4
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6μ‹œκ°„
8μ‹œκ°„
10μ‹œκ°„
12μ‹œκ°„
D
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"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDA(...TRUNCATED)
document_5
λ„μ„œκ΄€ μ•ˆλ‚΄λ°μŠ€ν¬μ˜ μ „ν™”λ²ˆν˜Έ λ’·μžλ¦¬λ‘œ μ˜¬λ°”λ₯Έ 것은?
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8513
8836
5678
D
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document_6
κ³„μ•½μ„œμ—μ„œ λ³΄μ¦κΈˆμ€ μ–Όλ§ˆμž…λ‹ˆκΉŒ?
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1000λ§Œμ›
1500λ§Œμ›
2000λ§Œμ›
A
document
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document_7
κ³„μ•½μ„œμ—μ„œ μ›” μž„λŒ€λ£ŒλŠ” μ–Όλ§ˆμž…λ‹ˆκΉŒ?
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120λ§Œμ›
150λ§Œμ›
C
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document_8
"μ—¬ν–‰ μ˜ˆμ•½ ν™•μΈμ„œμ—μ„œ ν˜Έν…”μ˜ 체크인 μ‹œκ°„μ€ λͺ‡ μ‹œλΌκ³  ν‘œκΈ°λ˜μ–΄ μžˆμŠ΅λ‹ˆκΉŒ(...TRUNCATED)
μ˜€μ „ 11μ‹œ
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C
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document_9
μ—¬ν–‰ μ˜ˆμ•½ ν™•μΈμ„œμ—μ„œ μ—¬ν–‰μ˜ μΆœλ°œμΌμ€ μ–Έμ œμΈκ°€μš”?
2023λ…„ 7μ›” 15일
2023λ…„ 7μ›” 22일
2023λ…„ 6μ›” 1일
2023λ…„ 8μ›” 15일
A
document
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K-DTCBench

We introduce K-DTCBench, a newly developed Korean benchmark featuring both computer-generated and handwritten documents, tables, and charts. It consists of 80 questions for each image type and two questions per image, summing up to 240 questions in total. This benchmark is designed to evaluate whether vision-language models can process images in different formats and be applicable for diverse domains. 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. The proportions of digital and hand-written images are equal, each constituting 50%.

For more details, Please refer to the VARCO-VISION technical report.

Category Image K-DTCBench
document question: λ³΄κ³ μ„œμ˜ μ£Όμš” λ‚΄μš©μ΄ μ•„λ‹Œ 것은 λ¬΄μ—‡μΈκ°€μš”?
A: μ•ˆμ „ 인프라 ν™•μΆ©
B: μž¬λ‚œ 및 사고 예방 체계 ꡬ좕
C: μ‹œλ―Ό μ•ˆμ „ ꡐ윑 κ°•ν™”
D: κΈ΄κΈ‰ λŒ€μ‘ μ‹œμŠ€ν…œ κ°œμ„ 
table question: 인프라 ꡬ좕 ν•­λͺ©μ˜ μ μˆ˜λŠ” λͺ‡ μ μΈκ°€μš”?
A: 4
B: 6
C: 8
D: 10
chart question: 직μž₯인듀이 퇴근 ν›„ 두 번째둜 μ„ ν˜Έν•˜λŠ” ν™œλ™μ€ λ¬΄μ—‡μΈκ°€μš”?
A: μš΄λ™
B: μ—¬κ°€ν™œλ™
C: 자기개발
D: νœ΄μ‹

Inference Prompt

<image>
{question}
Options: A: {A}, B: {B}, C: {C}, D: {D}
주어진 선택지 쀑 ν•΄λ‹Ή μ˜΅μ…˜μ˜ 문자둜 직접 λ‹΅ν•˜μ„Έμš”.

Results

Below are the evaluation results of various vision-language models, including VARCO-VISION-14B on K-DTCBench.

VARCO-VISION-14B Pangea-7B Pixtral-12B Molmo-7B-D Qwen2-VL-7B-Instruct LLaVA-One-Vision-7B
K-DTCBench 84.58 48.33 27.50 45.83 75.00 52.91

Citation

If you use K-DTCBench in your research, please cite the following:

@misc{ju2024varcovisionexpandingfrontierskorean,
      title={VARCO-VISION: Expanding Frontiers in Korean Vision-Language Models}, 
      author={Jeongho Ju and Daeyoung Kim and SunYoung Park and Youngjune Kim},
      year={2024},
      eprint={2411.19103},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2411.19103}, 
}
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