Improve dataset card: add metadata, paper link, and dataset details

README.md

@@ -1,5 +1,66 @@
 ---
 license: cc-by-nc-4.0
+task_categories:
+- text-classification
+- question-answering
+- summarization
+- text-retrieval
+- text-generation
+language:
+- zh
+tags:
+- legal
+- privacy
+- federated-learning
 ---
 
-Check the GitHub Repo for more information: https://github.com/SMILELab-FL/FedPII.
+# FedPII Dataset
+
+This repository contains the dataset for the paper [Simple Yet Effective: Extracting Private Data Across Clients in Federated Fine-Tuning of Large Language Models](https://huggingface.co/papers/2506.06060).
+
+FedPII is a Chinese legal-domain dataset with fine-grained PII (Personally Identifiable Information) annotations consistent with CPIS, GDPR, and CCPA standards. It is designed to evaluate privacy risks and cross-client data leakage in Federated Large Language Models (FedLLMs).
+
+- **GitHub Repository:** [SMILELab-FL/FedPII](https://github.com/SMILELab-FL/FedPII)
+
+## Dataset Description
+
+The dataset consists of 16,194 samples across various legal-related tasks. It includes fine-grained PII annotations (such as names, addresses, and birthdays) used to assess how easily private information can be recovered during federated learning.
+
+### Tasks and Statistics
+
+| Task | Description | Split | Samples | With PII |
+| :--- | :--- | :--- | :--- | :--- |
+| `leg_case_cls` | Legal Case Classification | Train/Test | 4,028 | 1,019 |
+| `jud_sum` | Judicial Summarization | Train/Test | 2,651 | 2,650 |
+| `sim_case_match` | Similar Case Matching | Train/Test | 3,773 | 3,771 |
+| `jud_read_compre` | Judicial Reading Comprehension | Train/Test | 3,498 | 3,493 |
+| `exam` | Legal Exam | Train/Test | 2,244 | 700 |
+
+**Total Samples:** 16,194 (14,575 Train / 1,619 Test).
+
+## Citation
+
+```bibtex
+@inproceedings{hu-etal-2025-simple,
+    title = "Simple Yet Effective: Extracting Private Data Across Clients in Federated Fine-Tuning of Large Language Models",
+    author = "Hu, Yingqi  and
+      Zhang, Zhuo  and
+      Zhang, Jingyuan  and
+      Wang, Jinghua  and
+      Wang, Qifan  and
+      Qu, Lizhen  and
+      Xu, Zenglin",
+    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
+    month = dec,
+    year = "2025",
+    address = "Mumbai, India",
+    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
+    url = "https://aclanthology.org/2025.findings-ijcnlp.113/",
+    pages = "1808--1827",
+    ISBN = "979-8-89176-303-6",
+}
+```
+
+## License
+
+This work is licensed under a [Creative Commons Attribution-NonCommercial 4.0 International License](https://creativecommons.org/licenses/by-nc/4.0/).