LEGAL ANALYTICS WITH LARGE LANGUAGE MODELS AND STRUCTURED KNOWLEDGE BASES

Authors

  • Fernando de Araujo Department of Computer Science & Engineering, Texas A&M University, College Station, USA
  • Edward Shen Department of Computer Science & Engineering, Texas A&M University, College Station, USA
  • John Stevens (Corresponding Author) Department of Computer Science, The University of Texas at Austin, Austin, USA

Keywords:

Legal analytics, Large language models, Structured knowledge bases

Abstract

The integration of legal analytics with large language models and structured knowledge bases is revolutionizing the legal profession by enhancing the efficiency and effectiveness of legal services. Legal analytics leverages data analysis techniques to extract insights from vast amounts of legal data, enabling legal professionals to make informed decisions and streamline operations. LLMs, such as OpenAI's GPT-3, provide advanced natural language processing capabilities that facilitate the analysis and generation of legal texts. When combined with structured knowledge bases, which organize legal information systematically, the potential for improved accuracy and sophisticated querying capabilities increases significantly. This paper explores the intersection of legal analytics, LLMs, and structured knowledge bases, emphasizing their roles in modern legal practice and the benefits of their integration. By examining the historical context, capabilities, and challenges associated with these technologies, we highlight the importance of embracing innovation to navigate the complexities of the evolving legal landscape. Ultimately, the synergy between legal analytics, LLMs, and structured knowledge bases promises to foster a more data-driven approach to law, improving client outcomes and enhancing the overall efficiency of legal practice.

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Published

2024-01-01

Issue

Vol. 2 No. 3 (2024)

Section

Research Article

DOI:

https://doi.org/10.61784/wjit3009

How to Cite

Araujo, F., Shen, E., Stevens, J. (2024). Legal Analytics With Large Language Models And Structured Knowledge Bases. Eurasia Journal of Science and Technology, 2(3), 18-26. https://doi.org/10.61784/wjit3009