Legal-Domain LLM

A Legal-Domain LLM is a domain-specific LLM that is designed specifically for the legal domain.

• Context:
  • It can (typically) be trained on a Legal-Domain Enriched LLM Corpus.
  • It can leverage a dedicated architecture such as the Mistral 7B architecture, undergoing extensive training on a legal corpus to achieve proficiency in legal document processing.
  • It can exhibit state-of-the-art performance in understanding and processing legal documents, showcasing potential to transform legal research and legal practice.
  • It can employ instructional fine-tuning methods utilizing legal datasets, enhancing its performance on domain-specific legal tasks.
  • It can be released under an open license such as the MIT License, promoting open access and encouraging further development and research at the intersection of AI and law.
  • It can focus on English-speaking jurisdictions for its training corpus, covering various legal systems.
  • It can introduce new benchmarks like LegalBench-Instruct and Legal-MMLU to evaluate the model's legal proficiency.
  • It can aim to empower legal professionals by providing a tool for navigating and interpreting the complex landscape of legal documents, potentially improving efficiency and accuracy in legal work.
  • ...
• Example(s):
  • SaulLM-7B, a large language model tailored for the legal domain.
  • LEGAL-BERT.
  • ...
• Counter-Example(s):
  • a Financial-Domain LLM, such as: Bloomberg LLM.
  • a General-Purpose LLMs such as: GPT-4 or BERT.
• See: Legal Text Comprehension, Legal Document Processing, AI in Legal Practice.

References

2024

• (Justo & Recario, 2024) ⇒ [[::Jenel M Justo]], and [[::Reginald Neil C Recario]]. ([[::2024]]). “Text Simplification System for Legal Contract Review.” In: Future of Information and Communication Conference. doi:10.1007/978-3-031-53960-2_8
  • QUOTE:
    • The paper explores the integration of automated contract review with text simplification to improve the readability of legal contracts and make them more accessible to non-experts. This process involves NLP techniques and machine learning models specifically tuned for the legal domain.
    • The paper leverages a language model fine-tuned on legal data to extract salient clauses from contracts. These clauses are then simplified using language models trained on legal documents, highlighting the use of deep learning in legal tech.
    • This research aimed to develop a text simplification system for legal contracts that could enhance the readability and understandability of automated contract review outputs using BERT models.

      In particular, we implemented automated contract review using DeBERTa-v2-xlarge-CUAD; implemented text simplifica - tion for contracts using LegalBERT and LegalBERT-Contract; evaluated the readability of system output using Flesch Kincaid Grade Level (FKGL), Flesch Reading Ease (FRE), Simple Measure Of Gobbledygook (SMOG) and Dale Chall (DC); and evaluated the simplicity, grammaticality, and meaning preservation of the system output through Likert scale assessment.

2024

• (Colombo et al., 2024) ⇒ Pierre Colombo, Telmo Pessoa Pires, Malik Boudiaf, Dominic Culver, Rui Melo, Caio Corro, Andre F. T. Martins, Fabrizio Esposito, Vera Lúcia Raposo, Sofia Morgado, and Michael Desa. (2024). "SaulLM-7B: A Pioneering Large Language Model for Law.” In: arXiv:2403.03883, doi:10.48550/arXiv.2403.03883
  • QUOTE: "In this paper, we introduce SaulLM-7B, a large language model (LLM) tailored for the legal domain. With 7 billion parameters, SaulLM-7B is the first LLM designed explicitly for legal text comprehension and generation."

2020

• (Chalkidis et al., 2020) ⇒ Ilias Chalkidis, Manos Fergadiotis, Prodromos Malakasiotis, Nikolaos Aletras, and Ion Androutsopoulos. (2020). “LEGAL-BERT: The Muppets Straight Out of Law School.” arXiv preprint arXiv:2010.02559 DOI:10.48550/arXiv.2010.02559.

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