Update content.py

content.py

@@ -13,9 +13,7 @@ Bottom_logo = f'''<img src="data:image/jpeg;base64,{bottom_logo}" style="width:2
 intro_md = f'''
 # {benchname} Leaderboard
 
-* [📊 Dataset](https://huggingface.co/datasets/maum-ai/KOFFVQA_Data)
-* [🧪 Evaluation Code](https://github.com/maum-ai/KOFFVQA)
-* [📄 Report](https://arxiv.org/abs/2503.23730)
+[**🏆 Leaderboard**](https://huggingface.co/spaces/maum-ai/KOFFVQA-Leaderboard) | [**📄 KOFFVQA Arxiv**](https://arxiv.org/abs/2503.23730) | [**🤗 KOFFVQA Dataset**](https://huggingface.co/datasets/maum-ai/KOFFVQA_Data)  
 
 {benchname}🔍 is a Free-Form VQA benchmark dataset designed to evaluate Vision-Language Models (VLMs) in Korean language environments. Unlike traditional multiple-choice or predefined answer formats, KOFFVQA challenges models to generate open-ended, natural-language answers to visually grounded questions. This allows for a more comprehensive assessment of a model's ability to understand and generate nuanced Korean responses.
 
@@ -34,22 +32,35 @@ The {benchname} benchmark is designed to evaluate and compare the performance of
 This benchmark includes a total of 275 Korean questions across 10 tasks. The questions are open-ended, free-form VQA (Visual Question Answering) with objective answers, allowing responses without strict format constraints.
 
 ## News
+* **2025-04-25** : Our [leaderboard](https://huggingface.co/spaces/maum-ai/KOFFVQA-Leaderboard) currently finished evaluating total **81** of famous vlm around open- or close- sourced model. Also refactoring the evaluation code to make it easier to use and evaluate much more diverse models.
 
-* **2025-04-01** : Our paper [KOFFVQA: An Objectively Evaluated Free-form VQA Benchmark for Large Vision-Language Models in the Korean Language](https://arxiv.org/abs/2503.23730) has released and accepted to CVPRW 2025, Workshop on Benchmarking and Expanding AI Multimodal Approaches(BEAM 2025) 🎉
+* **2025-04-01** : Our paper [KOFFVQA: An Objectively Evaluated Free-form VQA Benchmark for Large Vision-Language Models in the Korean Language](https://arxiv.org/abs/2503.23730) has been released and accepted to CVPRW 2025, Workshop on Benchmarking and Expanding AI Multimodal Approaches(BEAM 2025) 🎉
 
 * **2025-01-21**: [Evaluation code](https://github.com/maum-ai/KOFFVQA) and [dataset](https://huggingface.co/datasets/maum-ai/KOFFVQA_Data) release
 
 * **2024-12-06**: Leaderboard Release!
 
+## Citation
+
+**BibTeX:**
+```bibtex
+@article{kim2025koffvqa,
+  title={KOFFVQA: An Objectively Evaluated Free-form VQA Benchmark for Large Vision-Language Models in the Korean Language},
+  author={Kim, Yoonshik and Jung, Jaeyoon},
+  journal={arXiv preprint arXiv:2503.23730},
+  year={2025}
+}
+```
+
 '''.strip()
 
 submit_md = f'''
 
-# Submit (coming soon)
+# Submit
 
 We are not accepting model addition requests at the moment. Once the request system is established, we will start accepting requests.
 
-🚀 Curious how your VLM performs in Korean? Use our [Evaluation code](https://github.com/maum-ai/KOFFVQA) to run it on KOFFVQA and check the score.
+🚀 Wondering how your VLM stacks up in Korean? Just run it with our evaluation code and get your score—no API key needed!
 
 🧑‍⚖️ We currently use google/gemma-2-9b-it as the judge model, so there's no need to worry about API keys or usage fees.