Proceedings of the 2024 Brawijaya International Conference (BIC 2024)

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ASVRI-Legal: Fine-Tuning LLMs with Retrieval-Augmented Generation for Enhanced Legal Regulation

Authors
One Octadion1, *, Bondan Sapta Prakoso1, 2, Nanang Yudi Setiawan1, 2, Novanto Yudistira3
1Jalin Mayantara Indonesia, Malang, 65141, Indonesia
2Information System Department, Faculty of Computer Science, Universitas Brawijaya, Jl. Veteran, Malang, 65145, Indonesia
3Informatics Engineering Department, Faculty of Computer Science, Universitas Brawijaya, Jl. Veteran, Malang, 65145, Indonesia
*Corresponding author.
Corresponding Author
One Octadion
Available Online 11 November 2025.
DOI
10.2991/978-94-6463-854-7_19How to use a DOI?
Keywords
Fine-tuning; Large Language Models; Legal domain; Retrieval-Augmented Generation; Legal analysis; Regulation development
Abstract

In this study, we explore the fine-tuning of Large Language Models (LLMs) to better support policymakers in their crucial work of understanding, analyzing, and crafting legal regulations. To equip the model with a deep understanding of legal texts, we curated a supervised dataset tailored to the specific needs of the legal domain. Additionally, we integrated the Retrieval-Augmented Generation (RAG) method, enabling the LLM to access and incorporate up-to-date legal knowledge from external sources. This combination of fine-tuning and RAGbased augmentation results in a tool that not only processes legal information but actively assists policymakers in interpreting regulations and drafting new ones that align with current needs. The results demonstrate that this approach can significantly enhance the effectiveness of legal research and regulation development, offering a valuable resource in the ever-evolving field of law.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2024 Brawijaya International Conference (BIC 2024)
Series
Atlantis Advances in Applied Sciences
Publication Date
11 November 2025
ISBN
978-94-6463-854-7
ISSN
3091-4442
DOI
10.2991/978-94-6463-854-7_19How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - One Octadion
AU  - Bondan Sapta Prakoso
AU  - Nanang Yudi Setiawan
AU  - Novanto Yudistira
PY  - 2025
DA  - 2025/11/11
TI  - ASVRI-Legal: Fine-Tuning LLMs with Retrieval-Augmented Generation for Enhanced Legal Regulation
BT  - Proceedings of the 2024 Brawijaya International Conference (BIC 2024)
PB  - Atlantis Press
SP  - 245
EP  - 261
SN  - 3091-4442
UR  - https://doi.org/10.2991/978-94-6463-854-7_19
DO  - 10.2991/978-94-6463-854-7_19
ID  - Octadion2025
ER  -