Legal Syllogism Prompting: Teaching Large Language Models for Legal
  Judgment Prediction
Cong Jiang Xiaolei Yang
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.