  This study investigates the performance of the zero-shot method in
classifying data using three large language models, alongside two models with
large input token sizes and the two pre-trained models on legal data. Our main
dataset comes from the domain of U.S. civil procedure. It includes summaries of
legal cases, specific questions, potential answers, and detailed explanations
for why each solution is relevant, all sourced from a book aimed at law
students. By comparing different methods, we aimed to understand how
effectively they handle the complexities found in legal datasets. Our findings
show how well the zero-shot method of large language models can understand
complicated data. We achieved our highest F1 score of 64% in these experiments.
