Logical reasoning is fundamental for humans yet presents a substantial challenge in the domain of Artificial Intelligence. Initially, researchers used Knowledge Representation and Reasoning (KR) systems that did not scale and required non trivial manual effort. Recently, the emergence of large language models (LLMs) has demonstrated the ability to overcome various limitations of formal Knowledge Representation (KR) systems. Consequently, there is a growing interest in using LLMs for logical reasoning via natural language. This work strives to understand the proficiency of LLMs in logical reasoning by offering a brief review of the latest progress in this area; with a focus on the logical reasoning datasets, tasks, and the methods adopted to utilize LLMs for reasoning. To offer a thorough analysis, we have compiled a benchmark titled LogiGLUE. This includes 24 varied datasets encompassing deductive, abductive, and inductive reasoning. We have standardized these datasets into Seq2Seq tasks to facilitate straightforward training and evaluation for future research. Utilizing LogiGLUE as a foundation, we have trained an instruction fine tuned language model, resulting in LogiT5. We study single task training, multi task training, and a chain of thought knowledge distillation fine tuning technique to assess the performance of model across the different logical reasoning categories. By this comprehensive process, we aim to shed light on the capabilities and potential pathways for enhancing logical reasoning proficiency in LLMs, paving the way for more advanced and nuanced developments in this critical field.

通过对最新进展的简要回顾，本文试图了解大型语言模型在逻辑推理方面的能力，包括逻辑推理数据集、任务以及利用大型语言模型进行推理的方法。通过构建基准测试集LogiGLUE，并训练一个经过指令微调的语言模型LogiT5，研究了单任务训练、多任务训练以及思维链知识蒸馏微调技术在不同逻辑推理类别上的表现，旨在揭示大型语言模型在逻辑推理中的能力和潜在路径，为该关键领域的高级和精细化发展铺平道路。

LogiGLUE: 逻辑推理能力的语言模型分析调查和基准评测