Add link to paper, Github repository, ethics statement and acknowledgement.

This PR adds the link to the paper, the Github repository, the ethics statement and the acknowledgement to the dataset card.

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!