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.

该研究通过比较不同方法，调查了零样本方法在使用三种大型语言模型、两种具有大输入标记尺寸的模型和两种预训练的法律数据模型进行数据分类方面的表现。我们的主要数据集来自美国民事诉讼领域，包括法律案例摘要、具体问题、潜在答案和详细解释，这些都是从一本针对法律学生的书中获取的。通过这些实验，我们的发现展示了大型语言模型的零样本方法如何有效理解复杂数据，我们在实验中取得了最高的 F1 得分，达到了 64%。