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        <title>Algorithm for Automatic Legislative Text Consolidation</title>
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        <namePart type="given">Matias</namePart>
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            <title>Proceedings of the Natural Legal Language Processing Workshop 2024</title>
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            <namePart type="given">Nikolaos</namePart>
            <namePart type="family">Aletras</namePart>
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            <namePart type="given">Ilias</namePart>
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            <namePart type="given">Leslie</namePart>
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            <namePart type="given">Daniel</namePart>
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    <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, finetuned 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 HuggingFace. 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.</abstract>
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%0 Conference Proceedings
%T Algorithm for Automatic Legislative Text Consolidation
%A Etcheverry, Matias
%A Real, Thibaud
%A Chavallard, Pauline
%Y Aletras, Nikolaos
%Y Chalkidis, Ilias
%Y Barrett, Leslie
%Y Goanță, Cătălina
%Y Preoțiuc-Pietro, Daniel
%Y Spanakis, Gerasimos
%S Proceedings of the Natural Legal Language Processing Workshop 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, FL, USA
%F etcheverry-etal-2024-algorithm
%X 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, finetuned 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 HuggingFace. 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.
%R 10.18653/v1/2024.nllp-1.13
%U https://aclanthology.org/2024.nllp-1.13/
%U https://doi.org/10.18653/v1/2024.nllp-1.13
%P 166-175

Markdown (Informal)

[Algorithm for Automatic Legislative Text Consolidation](https://aclanthology.org/2024.nllp-1.13/) (Etcheverry et al., NLLP 2024)

• Algorithm for Automatic Legislative Text Consolidation (Etcheverry et al., NLLP 2024)

ACL

• Matias Etcheverry, Thibaud Real, and Pauline Chavallard. 2024. Algorithm for Automatic Legislative Text Consolidation. In Proceedings of the Natural Legal Language Processing Workshop 2024, pages 166–175, Miami, FL, USA. Association for Computational Linguistics.