Using Generative AI to Identify Arguments in Judges’ Reasons: Accuracy and Benefits for Students - Paul Burgess; Iwan Williams; Lizhen Qu; Weiqing Wang (Law, Technology and Humans)
This study evaluates the effectiveness of generative artificial intelligence (GAI) in identifying and reconstructing legal arguments from judges’ reasons in court cases, focusing on the practical implications for law students and legal educators. By examining the performance of two versions of popular Large Language Models – ChatGPT and Claude – across five recent High Court of Australia decisions, the study makes a preliminary assessment of the accuracy of LLM systems in replicating a skill essential for lawyers: identification of arguments and argument chains in judges’ reasons. The methodology involves marking LLM-generated outputs with reference to both a sample answer and a detailed rubric.