用生成模型自动合并法律条文，效率提升显著。
Algorithm for Automatic Legislative Text Consolidation
- 采用轻量量化生成模型+LoRA微调，实现法律文本自动修订
- 在复杂法案上达成超63%的成功率，数小时内完成全流程
- 首个将生成模型用于立法文本整合的研究，适合法律科技从业者
本研究提出一种自动化立法文本整合方法，解决传统人工合并耗时问题。采用轻量级量化生成模型并结合LoRA微调，实现对立法文本的准确修订。据作者所知，这是首次将生成模型应用于立法文本整合任务。数据集已公开于HuggingFace。实验表明，该方法可将完整立法文本整合流程压缩至数小时，对复杂法案的整合成功率超过63%。
原文摘要 · Abstract (English)
This study introduces a method for automating the consolidation process in a legal context, a time-consuming task traditionally performed by legal professionals. We present a generative approach that processes legislative texts to automatically apply amendments. Our method employs light quantized generative model, fine-tuned with LoRA, to generate accurate and reliable amended texts. To the authors knowledge, this is the first time generative models are used on legislative text consolidation. Our dataset is publicly available on HuggingFace1. Experimental results demonstrate a significant improvement in efficiency, offering faster updates to legal documents. A full automated pipeline of legislative text consolidation can be done in a few hours, with a success rate of more than 63% on a difficult bill.
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