法律三段论提示：教授大型语言模型进行法律判决预测
Legal Syllogism Prompting: Teaching Large Language Models for Legal Judgment Prediction
AI总结：
本文提出法律三段论提示LoT，通过让LLM按“法律—事实—判决”结构推理，在CAIL2018上零样本提升GPT-3判决预测性能并增强可解释性。
摘要：
法律三段论是法律专业人士常用于分析案件的一种演绎推理形式。在本文中，我们提出了法律三段论提示（LoT），这是一种简单的提示方法，用于教授大型语言模型（LLM）进行法律判决预测。LoT仅教导模型：在法律三段论中，大前提是法律，小前提是事实，结论是判决。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模型开展了零样本判决预测实验。结果表明，与基线以及思维链提示相比，采用LoT的LLM取得了更优性能；思维链提示是多种推理任务上的最先进提示方法。与其他方法相比，LoT使模型能够专注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LLM能够在预测判决的同时给出法律条文和理由，从而显著增强模型的可解释性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
