Abstract
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.
Abstract (translated)
逻辑推理是人类基本的思维活动,但在人工智能领域中却面临巨大的挑战。起初,研究人员使用无法扩展且需要大量手动努力的知识表示和推理(KR)系统。最近,大型语言模型(LLM)的出现已经证明了能够克服正式知识表示(KR)系统的各种限制的能力。因此,越来越多的人开始使用LLM来进行自然语言逻辑推理。这项工作旨在通过简要回顾该领域的最新进展,理解LLM在逻辑推理方面的熟练程度。我们焦点关注逻辑推理数据集、任务和利用LLM进行推理的方法。为了进行全面分析,我们汇编了一个基准名为LogiGLUE。该基准包括24个不同的数据集,涵盖了从演绎、归纳和推断推理的各种类型。我们将这些数据集标准化为Seq2Seq任务,以便于未来的研究和 straightforward的训练和评估。利用LogiGLUE作为基础,我们训练了一个优化的语言模型,结果为LogiT5。我们研究单一任务训练、多任务训练和思维知识蒸馏优化技术,以评估模型在不同逻辑推理类别中的表现。通过这种方式,我们旨在阐明LLM在逻辑推理方面的能力和潜在路径,为这个关键领域的更高级、精细的发展铺平道路。
URL
https://arxiv.org/abs/2310.00836
