Legal Argument

Legal argumentation research focuses on computationally modeling and analyzing the reasoning processes behind legal decisions, aiming to improve both the understanding and automation of legal tasks. Current research heavily utilizes large language models (LLMs) like BERT and GPT, often employing techniques such as few-shot learning, prompt engineering, and fine-tuning on specialized legal datasets to classify arguments, validate answers, and generate summaries. This work is significant for advancing natural language processing capabilities in a complex domain and has the potential to improve legal research, education, and even automate aspects of legal practice.