Algorithm for Automatic Legislative Text Consolidation

• URL: http://arxiv.org/abs/2501.16794v1
• Date: Tue, 28 Jan 2025 08:52:33 GMT
• Title: Algorithm for Automatic Legislative Text Consolidation
• Authors: Matias Etcheverry, Thibaud Real, Pauline Chavallard,
• Abstract summary: We present a generative approach that processes legislative texts to automatically apply amendments.<n>Our method employs light quantized generative model, fine-tuned with LoRA, to generate accurate and reliable amended texts.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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