融合三段论推理和知识增强的司法问答方法OA

Due to the limitations of large language models in deeply understanding legal knowledge and grasping the logical connections between legal provisions and judicial issues,their performance in the field of judicial question-answering is constrained.To address these challenges,a judicial question-answering method that integrates syllogistic reasoning and knowledge enhancement is proposed.The method first incorporates legal provisions as an external knowledge base to compensate for the model's deficiencies in judicial domain knowledge.Then,it constructs a chain of thought based on judicial syllogism to strengthen the model's ability to discern the inherent logical relationships between judicial issues and legal provisions.Finally,the large language model combines legal provisions and judicial syllogism for reasoning,yielding more accurate and legally consistent answers.Experimental results demonstrate that the proposed method improves performance by 10.1%and 12.4%on judicial examination multiple-choice questions and judicial consultation question-answering datasets,respectively,compared to baseline models,validating its effectiveness.