mlsanityData to 14 Oct 2026

Large Language Models Meet Legal Artificial Intelligence: A Survey

arXiv 2509.09969 ↗First posted 12 Sep 2025cs.CL cs.AIcited by 10

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Abstract (arXiv)

Large Language Models (LLMs) have significantly advanced the development of Legal Artificial Intelligence (Legal AI) in recent years, enhancing the efficiency and accuracy of legal tasks. To advance research and applications of LLM-based approaches in legal domain, this paper provides a comprehensive review of 16 legal LLMs series and 47 LLM-based frameworks for legal tasks, and also gather 15 benchmarks and 29 datasets to evaluate different legal capabilities. Additionally, we analyse the challenges and discuss future directions for LLM-based approaches in the legal domain. We hope this paper provides a systematic introduction for beginners and encourages future research in this field. Resources are available at https://github.com/ZhitianHou/LLMs4LegalAI.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 65 of its 114 references that are in the map. First posted on arXiv 12 Sep 2025.

Builds on

65 of 114 references.

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38 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 9.

10 papers.

  1. 2026LRAS: Advanced Legal Reasoning with Agentic Searchcited by 8
  2. 2026LLM Agents in Law: Taxonomy, Applications, and Challengescited by 5
  3. 2026LegalGraphRAG: Multi-Agent Graph Retrieval-Augmented Generation for Reliable Legal Reasoningcited by 5
  4. 2026LegalCiteBench: Evaluating Citation Reliability in Legal Language Modelscited by 3
  5. 2025ShiZhi: A Chinese Lightweight Large Language Model for Court View Generationcited by 1
  6. 2026Evaluation of Large Language Models in Legal Applications: Challenges, Methods, and Future Directionscited by 1
  7. 2026Towards Intelligent Legal Document Analysis: CNN-Driven Classification of Case Law Textscited by 0
  8. 2026Exploring Lightweight Large Language Models for Court View Generationcited by 0
  9. 2026JusticeAxis: Benchmarking Legal Judgment between Rigid Rule Application and Ungrounded Discretioncited by 0
  10. 2026AgentTune: A Unified Training Lifecycle for Tool-Using LLM Agentscited by 0