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How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence

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arxiv 2004.12158 v5 pith:LWN4J5GK submitted 2020-04-25 cs.CL

Haoxi Zhong , Chaojun Xiao , Cunchao Tu , Tianyang Zhang , Zhiyuan Liu , Maosong Sun This is my paper
classification cs.CL
keywords legallegalaiprofessionalsartificialintelligenceresearcherstasksbenefit
verification ladder T0 review T1 audit T2 compute T3 formal
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Legal Artificial Intelligence (LegalAI) focuses on applying the technology of artificial intelligence, especially natural language processing, to benefit tasks in the legal domain. In recent years, LegalAI has drawn increasing attention rapidly from both AI researchers and legal professionals, as LegalAI is beneficial to the legal system for liberating legal professionals from a maze of paperwork. Legal professionals often think about how to solve tasks from rule-based and symbol-based methods, while NLP researchers concentrate more on data-driven and embedding methods. In this paper, we introduce the history, the current state, and the future directions of research in LegalAI. We illustrate the tasks from the perspectives of legal professionals and NLP researchers and show several representative applications in LegalAI. We conduct experiments and provide an in-depth analysis of the advantages and disadvantages of existing works to explore possible future directions. You can find the implementation of our work from https://github.com/thunlp/CLAIM.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The Judge Variable: Challenging Judge-Agnostic Legal Judgment Prediction

    cs.CL 2025-07 reject novelty 4.0 of 10

    Models trained on individual judges' past child-custody rulings predict those judges' future rulings better than a model trained on all judges together, a result the paper reads as support for legal realism.

  2. When Large Language Models Meet Law: Dual-Lens Taxonomy, Technical Advances, and Ethical Governance

    cs.CL 2025-07 conditional novelty 4.0 of 10

    A literature review that classifies LLM-for-law research using a dual-lens taxonomy of Toulmin argumentation components and legal practitioner roles.

  3. A Data Science Approach to Calcutta High Court Judgments: An Efficient LLM and RAG-powered Framework for Summarization and Similar Cases Retrieval

    cs.IR 2025-06 reject novelty 4.0 of 10

    Fine-tuning Pegasus on LLM-annotated headnotes improves part of the legal summarization pipeline, and a RAG framework retrieves similar Calcutta High Court cases, though retrieval quality is never measured.

  4. LLMPR: A Novel LLM-Driven Transfer Learning based Petition Ranking Model

    cs.CL 2025-05 reject novelty 2.0 of 10

    A petition-ranking model that reports near-perfect accuracy, but its target ranking is derived from the same gap-days features it feeds the model, making the result circular.

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