Author(s)

Fernando de Araujo¹, Edward Shen¹, John Stevens^2*

Affiliation(s)

¹Department of Computer Science & Engineering, Texas A&M University, College Station, USA.

²Department of Computer Science, The University of Texas at Austin, Austin, USA.

Corresponding Author

John Stevens

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.

KEYWORDS

Legal analytics; Large language models; Structured knowledge bases

CITE THIS PAPER

Fernando de Araujo, Edward Shen, John Stevens. Legal analytics with large language models and structured knowledge bases. World Journal of Information Technology. 2024, 2(3): 18-26. DOI: https://doi.org/10.61784/wjit3009.

REFERENCES

[1] Dahl M, Magesh V, Suzgun M, et al. Large legal fictions: Profiling legal hallucinations in large language models. Journal of Legal Analysis, 2024, 16(1): 64-93.

[2] Johnson L. Legal Analytics: A New Frontier. Harvard Law Review, 2021, 134(2): 205-230.

[3] OpenAI. GPT-3: Language Models are Few-Shot Learners. 2020. https://arxiv.org/abs/2005.14165.

[4] BERT J. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. 2019. https://aclanthology.org/N19-1423/.

[5] Westlaw. Legal Research Made Easy. 2024. https://legal.thomsonreuters.com/westlaw.

[6] LexisNexis. Comprehensive Legal Research Solutions. 2024. https://legal.lexisnexis.com.

[7] Casetext. AI-Powered Legal Research. 2024. https://casetext.com.

[8] Ravel Law. Visualizing Case Law. 2024. https://ravellaw.com.

[9] Baker R. Predictive Analytics in Legal Practice. Legal Studies Quarterly, 2023, 12(1): 75-89.

[10] Drápal J, Westermann H, Savelka J. Using Large Language Models to Support Thematic Analysis in Empirical Legal Studies. In JURIX, 2023: 197-206.

[11] Legal Knowledge Graph. Creating a Structured Representation of Legal Knowledge. 2024.  https://legalknowledgegraph.org

[12] Chen J, Cui Y, Zhang X, et al. Temporal Convolutional Network for Carbon Tax Projection: A Data-Driven Approach. Applied Sciences, 2024, 14(20): 9213.

[13] Wang X, Hoo V, Liu M, et al. Advancing legal recommendation system with enhanced Bayesian network machine learning. Artificial Intelligence and Law, 2024: 1-18.

[14] Lu K, Zhang X, Zhai T, et al. Adaptive Sharding for UAV Networks: A Deep Reinforcement Learning Approach to Blockchain Optimization. Sensors, 2024, 24(22): 7279.

[15] Green T. Automation in Legal Practice: Opportunities and Challenges. Law Practice Management, 2020, 19(4): 200-215.

[16] Zhou Z, Shi J X, Song P X, et al. LawGPT: A Chinese Legal Knowledge-Enhanced Large Language Model. 2024. https://arxiv.org/abs/2406.04614.

[17] Steenhuis Q, Colarusso D, Willey B. Weaving Pathways for Justice with GPT: LLM-driven automated drafting of interactive legal applications. 2023. https://arxiv.org/abs/2312.09198.

[18] Guha N, Nyarko J, Ho D, et al. Legalbench: A collaboratively built benchmark for measuring legal reasoning in large language models. Advances in Neural Information Processing Systems, 2024, 36.

[19] Yue S, Chen W, Wang S, et al. Disc-lawllm: Fine-tuning large language models for intelligent legal services. 2023. https://arxiv.org/abs/2309.11325.

[20] Wang Y, Qian W, Zhou H, et al. Exploring new frontiers of deep learning in legal practice: A case study of large language models. International Journal of Computer Science and Information Technology, 2023, 1(1): 131-138.

[21] Wang X, Wu Y C. Empowering legal justice with AI: A reinforcement learning SAC-VAE framework for advanced legal text summarization. PloS one, 2024, 19(10): e0312623.

[22] Clark E. Data Privacy in Legal Technology. Journal of Cyber Law, 2022, 9(1): 45-60.

[23] Young D. Client-Centric Legal Services: A New Paradigm. Law and Society Review, 2023, 18(2): 80-95.

[24] Colombo P, Pires T P, Boudiaf M, et al. Saullm-7b: A pioneering large language model for law. 2024. https://arxiv.org/abs/2403.03883.

[25] Turner S. The Future of Law Firms: Adapting to Technology. Legal Business Review, 2020, 22(4): 120-135.

[26] Nay J J, Karamardian D, Lawsky S B, et al. Large language models as tax attorneys: a case study in legal capabilities emergence. Philosophical Transactions of the Royal Society A, 2024, 382(2270): 20230159.

[27] Yang X, Wang Z, Wang Q, et al. Large language models for automated q&a involving legal documents: a survey on algorithms, frameworks and applications. International Journal of Web Information Systems, 2024, 20(4): 413-435.

[28] Shumway D O, Hartman H J. Medical malpractice liability in large language model artificial intelligence: legal review and policy recommendations. Journal of osteopathic medicine, 2024, 124(7): 287-290.

[29] Robinson H. The Importance of Metadata in Legal Research. Journal of Legal Metadata, 2020, 6(2): 30-45.

[30] King B. AI in Legal Research: A Review of Current Tools. Journal of Legal Technology, 2022, 14(3): 175-190.

[31] Izzidien A, Sargeant H, Steffek F. LLM vs. Lawyers: Identifying a Subset of Summary Judgments in a Large UK Case Law Dataset. 2024. https://arxiv.org/abs/2403.04791.

[32] Scott M. Enhancing Legal Outcomes through Predictive Analytics. Journal of Legal Outcomes, 2023, 12(1): 15-30.

[33] Rahman N, Santacana E. Beyond fair use: Legal risk evaluation for training LLMs on copyrighted text. In ICML Workshop on Generative AI and Law. 2023.

[34] Cheong I, Xia K, Feng K K, et al. (A) I Am Not a Lawyer, But...: Engaging Legal Experts towards Responsible LLM Policies for Legal Advice. In The 2024 ACM Conference on Fairness, Accountability, and Transparency, 2024: 2454-2469.

[35] Bernsohn D, Semo G, Vazana Y, et al. LegalLens: Leveraging LLMs for Legal Violation Identification in Unstructured Text. 2024. https://arxiv.org/abs/2402.04335.

[36] Frasheri M, Bakhtiarnia A, Esterle L, et al. Are LLM-based methods good enough for detecting unfair terms of service? 2024. https://arxiv.org/abs/2409.00077.

[37] Jiao J, Afroogh S, Xu Y, et al. Navigating llm ethics: Advancements, challenges, and future directions. 2024. https://arxiv.org/abs/2406.18841.

[38] Huang J H, Yang C C, Shen Y, et al. Optimizing numerical estimation and operational efficiency in the legal domain through large language models. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024: 4554-4562.

[39] Wang X, Zhang X, Hoo V, Shao Z, Zhang X. LegalReasoner: A Multi-Stage Framework for Legal Judgment Prediction via Large Language Models and Knowledge Integration. IEEE Access, 2024.

[40] Satterfield N, Holbrooka P, Wilcoxa T. Fine-tuning Llama with Case Law Data to Improve Legal Domain Performance. Sciety, 2024. https://sciety.org/articles/activity/10.31219/osf.io/e6mjs.

[41] Qasem R, Tantour B, Maree M. Towards the exploitation of llm-based chatbot for providing legal support to palestinian cooperatives. 2023. https://arxiv.org/abs/2306.05827.

[42] Jayakumar T, Farooqui F, Farooqui L. Large Language Models are legal but they are not: Making the case for a powerful LegalLLM. 2023. https://arxiv.org/abs/2311.08890.