Data Augmented Pipeline for Legal Information Extraction and Reasoning
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915718581190656 |
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| author | Phuong, Nguyen Minh Nguyen, Ha-Thanh Zin, May Myo Satoh, Ken |
| author_facet | Phuong, Nguyen Minh Nguyen, Ha-Thanh Zin, May Myo Satoh, Ken |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_05609 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Data Augmented Pipeline for Legal Information Extraction and Reasoning Phuong, Nguyen Minh Nguyen, Ha-Thanh Zin, May Myo Satoh, Ken Computation and Language 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. |
| title | Data Augmented Pipeline for Legal Information Extraction and Reasoning |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2601.05609 |