Update README.md

README.md

@@ -1,3 +1,88 @@
----
-license: mit
----
+---
+license: mit
+task_categories:
+- text-generation
+language:
+- zh
+tags:
+- ACG
+- animation
+size_categories:
+- 1K<n<10K
+---
+
+# ACG-SimpleQA
+
+<p align="center">
+   🌐 <a href="https://prnake.github.io/ACG-SimpleQA/" target="_blank">Website</a> • 
+   🤗 <a href="https://huggingface.co/datasets/Papersnake/ACG-SimpleQA" target="_blank">Hugging Face</a>
+   <br>  
+   <a href="README.md">中文</a> | <a href="README_en.md">English</a>
+</p>
+
+**ACG-SimpleQA** is an objective knowledge question-answering dataset focused on the Chinese ACG (Animation, Comic, Game) domain, containing **4242** ~~auto-generated~~ carefully designed QA samples. This benchmark aims to evaluate large language models' factual capabilities in the ACG culture domain, featuring Chinese language, diversity, high quality, static answers, and easy evaluation.
+
+## 📢 Latest Updates
+
+- **\[2025.04.24\]** We officially release the ACG-SimpleQA dataset! Welcome to download it from 🤗[Hugging Face](https://huggingface.co/datasets/Papersnake/ACG-SimpleQA)
+
+## 💫 Introduction
+
+ACG-SimpleQA is a comprehensive benchmark designed to test large language models' factual knowledge in the ACG culture domain. The dataset features:
+
+* 🀄 **Chinese**: ACG-SimpleQA focuses on Chinese ACG knowledge, providing a thorough evaluation of LLMs' factual abilities in this area.
+* 🍀 **Diversity**: Covers multiple subdomains such as anime, games, manga, and music, ensuring comprehensive assessment.
+* ⚡ **High Quality**: Strict quality control ensures the accuracy of questions and answers.
+* 💡 **Static**: All reference answers are time-invariant, and the knowledge cutoff is before 2024, ensuring long-term validity.
+* 🗂️ **Easy Evaluation**: The evaluation method is consistent with [SimpleQA](https://github.com/openai/simple-evals) and [ChineseSimpleQA](https://github.com/OpenStellarTeam/ChineseSimpleQA).
+
+## 📊 Leaderboard
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/prnake/ACG-SimpleQA/refs/heads/gh-pages/static/images/leaderboard.png?token=GHSAT0AAAAAACJSKQM7KFQUBCSSBAXTLY7U2AJFTAA" width="800px"/>
+</p>
+
+| Model                          | ACG-SimpleQA |
+|--------------------------------|--------------|
+| gemini-2.5-pro-preview-03-25   | 0.5434       |
+| gpt-4.5-preview                | 0.4884       |
+| gemini-2.5-flash-preview-04-17 | 0.3993       |
+| deepseek-v3-241226             | 0.3963       |
+| deepseek-v3-250324             | 0.3944       |
+| gpt-4.1                        | 0.3880       |
+| grok-3-beta                    | 0.3810       |
+| chatgpt-4o-latest              | 0.3758       |
+| gemini-2.0-flash-001           | 0.3597       |
+| minimax-01                     | 0.3175       |
+| gemini-2.0-flash-lite-001      | 0.2897       |
+| claude-3.7-sonnet              | 0.2864       |
+| glm-4-plus                     | 0.2659       |
+| claude-3.5-sonnet              | 0.2515       |
+| qwen-max                       | 0.2466       |
+| doubao-1.5-pro-32k-250115      | 0.2435       |
+| grok-3-mini-beta               | 0.2357       |
+| o4-mini                        | 0.2263       |
+| llama-4-maverick               | 0.1655       |
+| gpt-4.1-mini                   | 0.1610       |
+| glm-4-air-250414               | 0.1412       |
+| claude-3.5-haiku               | 0.1334       |
+| gemma-3-27b-it                 | 0.1247       |
+| llama-3.3-70b-instruct         | 0.1106       |
+| qwq-32b                        | 0.0974       |
+| qwen2.5-32b-instruct           | 0.0969       |
+| gpt-4.1-nano                   | 0.0957       |
+| glm-4-flash-250414             | 0.0700       |
+| gemma-3-4b-it                  | 0.0370       |
+
+## 📜 Citation
+
+If you use this repository in your research, please consider citing:
+
+```bibtex
+@misc{pka2025acgsimpleqa,
+    title={ACG-SimpleQA},
+    author={Papersnake},
+    howpublished = {\url{https://github.com/prnake/ACG-SimpleQA}},
+    year={2025}
+}
+```