Add link to paper, Github repository, ethics statement and acknowledgement. (#2)

- Add link to paper, Github repository, ethics statement and acknowledgement. (83e65fe95e16acb41fb340a03a951174a73b73f0)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -1,25 +1,24 @@
 ---
-license: cc-by-nc-4.0
-task_categories:
-- text-classification
 language:
 - zh
+license: cc-by-nc-4.0
 size_categories:
 - 10K<n<100K
+task_categories:
+- text-classification
 ---
+
 <h1 align="center"> ChineseHarm-bench</h1>
 <h3 align="center"> A Chinese Harmful Content  Detection Benchmark </h3>
 
 > ⚠️ **WARNING**: This project and associated data contain content that may be toxic, offensive, or disturbing. Use responsibly and with discretion.
 
 <p align="center">
-  <a href="">Project</a> •
-  <a href="">Paper</a> •
+  <a href="https://github.com/zjunlp/ChineseHarm-bench">Project</a> •
+  <a href="https://arxiv.org/abs/2506.10960">Paper</a> •
   <a href="https://huggingface.co/collections/zjunlp/chineseharm-bench-683b452c5dcd1d6831c3316c">Hugging Face</a> 
 </p>
 
-
-
 <div>
 </div>
 <div align="center">
@@ -39,6 +38,19 @@ Each file is a list of examples with:
 * `"文本"`: the input Chinese text
 * `"标签"`: the ground-truth label
 
+## 🚩 Ethics Statement
+
+We obtain all data with proper authorization from the respective data-owning organizations and signed the necessary agreements.
+
+**The benchmark is released under the CC BY-NC 4.0 license.
+All datasets have been anonymized and reviewed by the Institutional Review Board (IRB) of the data provider to ensure privacy protection.**
+
+Moreover, we categorically denounce any malicious misuse of this benchmark and are committed to ensuring that its development and use consistently align with human ethical principles.
+
+## Acknowledgement
+
+We gratefully acknowledge Tencent for providing the dataset and LLaMA-Factory for the training codebase.
+
 ## 🚩Citation
 
 Please cite our repository if you use ChineseHarm-bench in your work. Thanks!