Legal syllogism is a form of deductive reasoning commonly used by legal professionals to analyze cases. In this paper, we propose legal syllogism prompting (LoT), a simple prompting method to teach large language models (LLMs) for legal judgment prediction. LoT teaches only that in the legal syllogism the major premise is law, the minor premise is the fact, and the conclusion is judgment. Then the models can produce a syllogism reasoning of the case and give the judgment without any learning, fine-tuning, or examples. On CAIL2018, a Chinese criminal case dataset, we performed zero-shot judgment prediction experiments with GPT-3 models. Our results show that LLMs with LoT achieve better performance than the baseline and chain of thought prompting, the state-of-art prompting method on diverse reasoning tasks. LoT enables the model to concentrate on the key information relevant to the judgment and to correctly understand the legal meaning of acts, as compared to other methods. Our method enables LLMs to predict judgment along with law articles and justification, which significantly enhances the explainability of models.

翻译：法律三段论是法律专业人士分析案件时常用的一种演绎推理形式。本文提出法律三段论提示法（LoT），一种简单有效的提示方法，用于指导大型语言模型（LLMs）进行法律判决预测。LoT仅提示法律三段论中，大前提为法律条文，小前提为案件事实，结论为判决结果。随后，模型无需任何学习、微调或示例，即可生成案件的三段论推理过程并给出判决。在中文刑事案件数据集CAIL2018上，我们使用GPT-3模型进行了零样本判决预测实验。结果表明，采用LoT的LLMs在性能上优于基线方法以及当前各类推理任务上最先进的提示方法——思维链提示法。与其他方法相比，LoT使模型能够聚焦于与判决相关的关键信息，并正确理解行为的法律含义。我们的方法使LLMs能够同时预测判决结果、相关法律条文及推理依据，显著提升了模型的可解释性。