mlsanityData to 7 Oct 2026

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

arXiv 2511.03563 ↗First posted 5 Nov 2025cs.CLcited by 1

On the map: Retrieval-augmented generation → Legal and financial RAG

Abstract (arXiv)

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 RAG-based 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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Legal and financial RAG, one of 15 directions in Retrieval-augmented generation. Builds on 15 of its 22 references that are in the map. First posted on arXiv 5 Nov 2025.

Builds on

15 of 22 references.

References · 15

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  5. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
  6. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,126
  7. 2023Instruction Tuning for Large Language Models: A Surveycited by 617
  8. 2024SaulLM-7B: A pioneering Large Language Model for Lawcited by 136
  9. 2023Chatlaw: A Multi-Agent Legal Assistant based on a Role-Aligned Mixture-of-Experts Architecturecited by 119
  10. 2021Lawformer: A Pre-trained Language Model for Chinese Legal Long Documentscited by 115
  11. 2024Reducing hallucination in structured outputs via Retrieval-Augmented Generationcited by 92
  12. 2024Synthetic continued pretrainingcited by 56
3 more
  1. 2024Fine-tuning and Utilization Methods of Domain-specific LLMscited by 35
  2. 2024The Chronicles of RAG: The Retriever, the Chunk and the Generatorcited by 34
  3. 2024LawGPT: A Chinese Legal Knowledge-Enhanced Large Language Modelcited by 34

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Cloud and On-Premises Deployment of Uzbek Legal RAG via Targeted Retriever Fine-Tuningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Enhancing Legal LLMs through Metadata-Enriched RAG Pipelines and Direct Preference Optimization
  2. 2025All for law and law for all: Adaptive RAG Pipeline for Legal Research
  3. 2026Fine-grained Claim-level RAG Benchmark for Law
  4. 2024LegalBench-RAG: A Benchmark for Retrieval-Augmented Generation in the Legal Domain
  5. 2025Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps
  6. 2025LRAGE: Legal Retrieval Augmented Generation Evaluation Tool
  7. 2024HyPA-RAG: A Hybrid Parameter Adaptive Retrieval-Augmented Generation System for AI Legal and Policy Applications
  8. 2025Elevating Legal LLM Responses: Harnessing Trainable Logical Structures and Semantic Knowledge with Legal Reasoning