可解释性逻辑推理数据集的构建和研究OA北大核心

Logical reasoning ability is crucial to understand natural language by machines and humans.The explanation of logical reasoning problems is an elaboration and description of the logical reasoning process.However,such explana-tions are lacking in current logical reasoning benchmarks.For this problem,this paper creates a Chinese and English dataset called explainable logical reasoning(Ex-LoR).This dataset contains 3411 logical reasoning problems with explanation data,and categorizes these problems into 6 classes according to their reasoning methods.This paper designs two tasks:logical reasoning question and answer task,and explanation generation task.Subsequently,this paper conducts experi-ments and analysis on this dataset by using several language models.The results show that the existing language models are still unable to well answer logical reasoning questions and generate reasonable explanations.Therefore,it is challenging to equip machines with logical reasoning capabilities.The logical reasoning dataset and experimental results presented in this paper can be used as a benchmark for subsequent research.