{
  "annotation_id": "1f69fd78-f9aa-4d13-8b44-bdb3079f535c",
  "date_created": "2026-02-17T05:53:04.953000Z",
  "date_modified": "2026-02-17T05:53:04.953000Z",
  "file_hash": "146c2688ee1f1549987900b2d8cff845b77efd9420e622cd94f34ac091edf9ad",
  "private": false,
  "record": {
    "abstract": "In this paper, we propose a pipeline leveraging Large Language Models (LLMs) for data augmentation in Information Extraction tasks within the legal domain. The proposed method is both simple and effective, significantly reducing the manual effort required for data annotation while enhancing the robustness of Information Extraction systems. Furthermore, the method is generalizable, making it applicable to various Natural Language Processing (NLP) tasks beyond the legal domain.",
    "arxiv_id": "2601.05609",
    "authors": [
      "Nguyen Minh Phuong",
      "Ha-Thanh Nguyen",
      "May Myo Zin",
      "Ken Satoh"
    ],
    "categories": [
      "cs.CL"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Data Augmented Pipeline for Legal Information Extraction and Reasoning",
    "url": "https://arxiv.org/abs/2601.05609",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "f4fcd919-55aa-4140-86ed-9314b99751a5",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}