{
 "id": "FedPII/FedPII-dataset",
 "owner": "FedPII",
 "name": "FedPII-dataset",
 "downloads": 70,
 "likes": 0,
 "last_modified": "2026-06-04T13:11:37.000Z",
 "stated_licence": "cc-by-nc-4.0",
 "stated_from": "tag",
 "licence_name": null,
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 "gated": false,
 "tasks": [
  "text-classification",
  "question-answering",
  "summarization",
  "text-retrieval",
  "text-generation"
 ],
 "citation": "@inproceedings{hu-etal-2025-simple,\n    title = \"Simple Yet Effective: Extracting Private Data Across Clients in Federated Fine-Tuning of Large Language Models\",\n    author = \"Hu, Yingqi  and\n      Zhang, Zhuo  and\n      Zhang, Jingyuan  and\n      Wang, Jinghua  and\n      Wang, Qifan  and\n      Qu, Lizhen  and\n      Xu, Zenglin\",\n    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\",\n    month = dec,\n    year = \"2025\",\n    address = \"Mumbai, India\",\n    publisher = \"The Asian Federation of Natural Language Processing and The Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2025.findings-ijcnlp.113/\",\n    pages = \"1808--1827\",\n    ISBN = \"979-8-89176-303-6\",\n}",
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 "reason": "The declared licence (cc-by-nc-4.0) is classified as having use restrictions.",
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}