A legal knowledge graph constructed from court cases, judgments, laws and other legal documents can enable a number of applications like question answering, document similarity, and search. While the use of knowledge graphs for distant supervision in NLP tasks is well researched, using knowledge graphs for applications like case similarity presents challenges. In this work, we describe our solution for predicting similar cases in Indian court judgements. We present our results and also discuss the impact of large language models on this task.
LawGPT: A Chinese Legal Knowledge-Enhanced Large Language Model
National Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；School of Artifical Intelligence, Nanjing University（南京大学人工智能学院）；School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）
LawGPT: Knowledge-Guided Data Generation and Its Application to Legal LLM
National Key Laboratory for Novel Software Technology（现代软件工程国家重点实验室）；School of Artificial Intelligence, Nanjing University（南京大学人工智能学院）；School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）
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）
When Do LLMs Apply the Wrong Law? Diagnosing LLM Failures in Temporal Legal Reasoning
Institute of Science Tokyo（东京科学大学）；Osaka University（大阪大学）；NII LLMC（日本信息学研究所语言、语言学与媒体中心）；Nara Institute of Science and Technology（奈良科学技术研究所）；Center of Juris-Informatics, ROIS-DS（日本学术研究推进机构跨学科科学研究中心法学信息学中心）
University of York（约克大学）；University of Sheffield（谢菲尔德大学）
Natural language processing (NLP) methods for analyzing legal text offer legal scholars and practitioners a range of tools allowing to empirically analyze law on a large scale. However, researchers seem to struggle when it comes to identifying ethical limits to using NLP systems for acquiring genuine insights both about the law and the systems' predictive capacity. In this paper we set out a number of ways in which to think systematically about such issues. We place emphasis on three crucial normative parameters which have, to the best of our knowledge, been underestimated by current debates: …
Reliability by design: quantifying and eliminating fabrication risk in LLMs. From generative to consultative AI: a comparative analysis in the legal domain and lessons for high-stakes knowledge bases
Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps
The University of Texas at Dallas（德克萨斯大学达拉斯分校）；Alabama Transportation Institute, The University of Alabama（阿拉巴马大学阿拉巴马交通研究所）；The University of Alabama（阿拉巴马大学）；Clemson University（克莱姆森大学）
Conditioning Large Language Models on Legal Systems? Detecting Punishable Hate Speech
Central Office for Information Technology in the Security Sector (ZITiS)（安全领域信息技术中央办公室（ZITiS））；Computational Linguistics, CATALPA, FernUniversität in Hagen（计算语言学、CATALPA、费尔本大学）；Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）；Waseda Institute for Advanced Study, Waseda University（早稻田大学高级研究机构）
Project Alexandria: Towards Freeing Scientific Knowledge from Copyright Burdens via LLMs
LAION；Open-Ψ (Open-Sci) Collective（Open-Ψ（Open-Sci）集体）；L3S Research Center, Leibniz University Hannover（汉诺威莱布尼茨大学L3S研究中心）；Stanford University（斯坦福大学）；TIB—Leibniz Information Centre for Science and Technology（莱布尼茨科技信息中心）；Juelich Supercomputing Center（于利希超级计算中心）；Technical University of Munich（慕尼黑工业大学）
International Institute of Information Technology, Hyderabad（海得拉巴国际信息技术学院）；Adobe Research（奥多比研究院）
Clause recommendation is the problem of recommending a clause to a legal contract, given the context of the contract in question and the clause type to which the clause should belong. With not much prior work being done toward the generation of legal contracts, this problem was proposed as a first step toward the bigger problem of contract generation. As an open-ended text generation problem, the distinguishing characteristics of this problem lie in the nature of legal language as a sublanguage and the considerable similarity of textual content within the clauses of a specific type. This simil…
An Integrated Framework of Prompt Engineering and Multidimensional Knowledge Graphs for Legal Dispute Analysis
School of Software, Yunnan University（软件学院，云南大学）；Yunnan Provincial Key Laboratory of Software Engineering（云南省软件工程重点实验室）；School of Law, Yunnan University（法学院，云南大学）
NeuroSymbolic AI for Legal AI-TRISM: Trustworthy, Reliable, Interpretable, Safe Models
Department of Computer Science, AI Institute, University of South Carolina（南卡罗来纳大学计算机科学系，人工智能研究所）；Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校计算机科学与电气工程系）；Department of Computer Science and Engineering, The Ohio State University（俄亥俄州立大学计算机科学与工程系）
Zhejiang University（浙江大学）；AZFT Joint Lab for Knowledge Engine（AZFT知识引擎联合实验室）；Hangzhou Innovation Center, Zhejiang University（浙江大学杭州创新中心）；Alibaba Group（阿里巴巴集团）
Recent years have witnessed the prosperity of legal artificial intelligence with the development of technologies. In this paper, we propose a novel legal application of legal provision prediction (LPP), which aims to predict the related legal provisions of affairs. We formulate this task as a challenging knowledge graph completion problem, which requires not only text understanding but also graph reasoning. To this end, we propose a novel text-guided graph reasoning approach. We collect amounts of real-world legal provision data from the Guangdong government service website and construct a leg…
The use of Structured English as a computation independent knowledge representation format for non-technical users in business rules representation has been proposed in OMGs Semantics and Business Vocabulary Representation (SBVR). In the legal domain we face a similar problem. Formal representation languages, such as OASIS LegalRuleML and legal ontologies (LKIF, legal OWL2 ontologies etc.) support the technical knowledge engineer and the automated reasoning. But, they can be hardly used directly by the legal domain experts who do not have a computer science background. In this paper we adapt t…
Operationalising Extended Cognition: Formal Metrics for Corporate Knowledge and Legal Accountability
Centre for Quantum Software and Information（量子软件与信息中心）；Institute for Clarity in Documentation（文档清晰度研究所）；Inria Paris-Rocquencourt（法国巴黎-罗克琴堡研究所）；Rajiv Gandhi University（拉吉夫·甘地大学）；Tsinghua University（清华大学）；Palmer Research Laboratories（帕勒研究中心）
State Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；Amazon Alexa AI（亚马逊 Alexa AI）
Automatically recommending relevant law articles to a given legal case has attracted much attention as it can greatly release human labor from searching over the large database of laws. However, current researches only support coarse-grained recommendation where all relevant articles are predicted as a whole without explaining which specific fact each article is relevant with. Since one case can be formed of many supporting facts, traversing over them to verify the correctness of recommendation results can be time-consuming. We believe that learning fine-grained correspondence between each sin…
The economics of smaller budgets and larger case numbers necessitates the use of AI in legal proceedings. We examine the concept of disparate impact and how biases in the training data lead to the search for fairer AI. This paper seeks to begin the discourse on what such an implementation would actually look like with a criticism of pre-processing methods in a legal context . We outline how pre-processing is used to correct biased data and then examine the legal implications of effectively changing cases in order to achieve a fairer outcome including the black box problem and the slow encroach…
A key challenge in the legal domain is the adaptation and representation of the legal knowledge expressed through texts, in order for legal practitioners and researchers to access this information easier and faster to help with compliance related issues. One way to approach this goal is in the form of a taxonomy of legal concepts. While this task usually requires a manual construction of terms and their relations by domain experts, this paper describes a methodology to automatically generate a taxonomy of legal noun concepts. We apply and compare two approaches on a corpus consisting of statut…
University of Bamberg（班贝格大学）；Freie Universität Berlin（柏林自由大学）；Université du Luxembourg（卢森堡大学）
The logico-pluralist LogiKEy knowledge engineering methodology and framework is applied to the modelling of a theory of legal balancing in which legal knowledge (cases and laws) is encoded by utilising context-dependent value preferences. The theory obtained is then used to formalise, automatically evaluate, and reconstruct illustrative property law cases (involving appropriation of wild animals) within the Isabelle proof assistant system, illustrating how LogiKEy can harness interactive and automated theorem proving technology to provide a testbed for the development and formal verification o…
