---
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="https://github.com/prnake/ACG-SimpleQA/blob/gh-pages/README.md">中文</a> | <a href="https://github.com/prnake/ACG-SimpleQA/blob/gh-pages/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}
}
```