Large language models (LLMs) have transformed many fields, including natural language processing, computer vision, and reinforcement learning. These models have also made a significant impact in the field of law, where they are being increasingly utilized to automate various legal tasks, such as legal judgement prediction, legal document analysis, and legal document writing. However, the integration of LLMs into the legal field has also raised several legal problems, including privacy concerns, bias, and explainability. In this survey, we explore the integration of LLMs into the field of law. We discuss the various applications of LLMs in legal tasks, examine the legal challenges that arise from their use, and explore the data resources that can be used to specialize LLMs in the legal domain. Finally, we discuss several promising directions and conclude this paper. By doing so, we hope to provide an overview of the current state of LLMs in law and highlight the potential benefits and challenges of their integration.
JUREX-4E: Juridical Expert-Annotated Four-Element Knowledge Base for Legal Reasoning
School of Law, Tsinghua University（清华大学法学院）；Wangxuan Institute of Computer Technology, Peking University（北京大学王璇计算机技术研究所）；Department of Legal Studies, University of Bologna（博洛尼亚大学法律系）
AgentsCourt: Building Judicial Decision-Making Agents with Court Debate Simulation and Legal Knowledge Augmentation
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
InSaAF: Incorporating Safety through Accuracy and Fairness | Are LLMs ready for the Indian Legal Domain?
Centre for Responsible AI, Indian Institute of Technology Madras（印度马德拉斯理工学院负责任人工智能中心）；International Institute of Information Technology, Hyderabad（海德拉巴国际信息技术学院）；AmexAI Labs, American Express（美国运通AmexAI实验室）；Wadhwani School of Data Science and AI, Indian Institute of Technology Madras（印度马德拉斯理工学院瓦德瓦尼数据科学与人工智能学院）
TransformLLM: Adapting Large Language Models via LLM-Transformed Reading Comprehension Text
School of Mathematical Sciences（数学科学学院）；Tel Aviv University（特拉维夫大学）；Department of Electrical Engineering（电气工程系）；The Faculty of Engineering（工程学院）；Bar Ilan University（巴伊兰大学）
University of Moratuwa（莫拉图瓦大学）；University of Colombo（科伦坡大学）
Aspect-Based Sentiment Analysis (ABSA) has been prominent and ongoing research over many different domains, but it is not widely discussed in the legal domain. A number of publicly available datasets for a wide range of domains usually fulfill the needs of researchers to perform their studies in the field of ABSA. To the best of our knowledge, there is no publicly available dataset for the Aspect (Party) Based Sentiment Analysis for legal opinion texts. Therefore, creating a publicly available dataset for the research of ABSA for the legal domain can be considered as a task with significant im…
Exploring the psychology of LLMs' Moral and Legal Reasoning
INSPER Institute of Education and Research（INSPER 教育与研究学院）；FGV Direito Rio（FGV 里约法学院）；PUC-Rio（里约热内卢天主教大学）；Center for Humans and Machines, Max Planck Institute for Human Development（马克斯·普朗克人类发展研究所人类与机器中心）；Ruhr-University Bochum（波鸿鲁尔大学）；Federal University of Rio de Janeiro（里约热内卢联邦大学）；State University of Rio de Janeiro（里约热内卢州立大学）
Despite the 'apparent clarity' of a given legal provision, its application may result in an outcome that does not exactly conform to the semantic level of a statute. The vagueness within a legal text is induced intentionally to accommodate all possible scenarios under which such norms should be applied, thus making the role of pragmatics an important aspect also in the representation of a legal norm and reasoning on top of it. The notion of pragmatics considered in this paper does not focus on the aspects associated with judicial decision making. The paper aims to shed light on the aspects of …
Attorneys and others have a strong interest in having a digital library with suitable services (e.g., summarizing, searching, and browsing) to help them work with large corpora of legal depositions. Their needs often involve understanding the semantics of such documents. That depends in part on the role of the deponent, e.g., plaintiff, defendant, law enforcement personnel, expert, etc. In the case of tort litigation associated with property and casualty insurance claims, such as relating to an injury, it is important to know not only about liability, but also about events, accidents, physical…
VLSP 2023 -- LTER: A Summary of the Challenge on Legal Textual Entailment Recognition
Vietnam Judicial Academy（越南司法学院）；Hanoi Law University（河内法律大学）；Hung Yen University of Technology and Education（兴安工业大学）；Ho Chi Minh City University of Science（胡志明市科学大学）；Japan Advanced Institute of Science and Technology（日本先进科学技术学院）；The Institute of Statistical Mathematics（统计数学研究所）；National Institute of Informatics（情报学研究所）
Improving Vietnamese Legal Question--Answering System based on Automatic Data Enrichment
VNU University of Engineering and Technology（越南国立大学工程技术大学）；National Institute of Informatics（国立信息学研究所）；Japan Advanced Institute of Science and Technology（日本北陆先端科学技术大学院大学）
University College Dublin（都柏林大学学院）；Birkbeck, University of London（伦敦大学伯贝克学院）
The contextual word embedding model, BERT, has proved its ability on downstream tasks with limited quantities of annotated data. BERT and its variants help to reduce the burden of complex annotation work in many interdisciplinary research areas, for example, legal argument mining in digital humanities. Argument mining aims to develop text analysis tools that can automatically retrieve arguments and identify relationships between argumentation clauses. Since argumentation is one of the key aspects of case law, argument mining tools for legal texts are applicable to both academic and non-academi…
Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Bucerius Law School（汉堡博锐思法学院）；Stanford Law School（斯坦福法学院）；Ruprecht-Karls-Universität Heidelberg（海德堡大学）；Hasso Plattner Institute, University of Potsdam（波茨坦大学哈索·普拉特纳研究所）；Illinois Tech – Chicago Kent College of Law（伊利诺伊理工大学芝加哥肯特法学院）
Building on the computer science concept of code smells, we initiate the study of law smells, i.e., patterns in legal texts that pose threats to the comprehensibility and maintainability of the law. With five intuitive law smells as running examples - namely, duplicated phrase, long element, large reference tree, ambiguous syntax, and natural language obsession -, we develop a comprehensive law smell taxonomy. This taxonomy classifies law smells by when they can be detected, which aspects of law they relate to, and how they can be discovered. We introduce text-based and graph-based methods to …
MARRO: Multi-headed Attention for Rhetorical Role Labeling in Legal Documents
University of Utah（犹他大学）；Bosch Research（博世研究院）；Indian Institute of Technology Kharagpur（印度克勒格布尔理工学院）；Indian Institute of Science Education and Research Kolkata（印度加尔各答科学教育与研究学院）
