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Found 11 papers, 9 papers with code
The KL3M Data Project: Copyright-Clean Training Resources for Large Language Models
Practically all large language models have been pre-trained on data that is subject to global uncertainty related to copyright infringement and breach of contract.
LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development
To this end, we release a multinational English legal corpus (LeXFiles) and a legal knowledge probing benchmark (LegalLAMA) to facilitate training and detailed analysis of legal-oriented PLMs.
Natural Language Processing in the Legal Domain
To support our analysis, we construct and analyze a nearly complete corpus of more than six hundred NLP & Law related papers published over the past decade.
GPT as Knowledge Worker: A Zero-Shot Evaluation of (AI)CPA Capabilities
The global economy is increasingly dependent on knowledge workers to meet the needs of public and private organizations.
GPT Takes the Bar Exam
Nearly all jurisdictions in the United States require a professional license exam, commonly referred to as "the Bar Exam," as a precondition for law practice.
Law Smells: Defining and Detecting Problematic Patterns in Legal Drafting
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.
LexGLUE: A Benchmark Dataset for Legal Language Understanding in English
Laws and their interpretations, legal arguments and agreements\ are typically expressed in writing, leading to the production of vast corpora of legal text.
Ranked #1 on
Natural Language Understanding
on LexGLUE
OpenEDGAR: Open Source Software for SEC EDGAR Analysis
OpenEDGAR is an open source Python framework designed to rapidly construct research databases based on the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system operated by the US Securities and Exchange Commission (SEC).
LexNLP: Natural language processing and information extraction for legal and regulatory texts
LexNLP is an open source Python package focused on natural language processing and machine learning for legal and regulatory text.
A General Approach for Predicting the Behavior of the Supreme Court of the United States
Building on developments in machine learning and prior work in the science of judicial prediction, we construct a model designed to predict the behavior of the Supreme Court of the United States in a generalized, out-of-sample context.
Physics and Society
