In this paper, we tackle the significant challenge of temporal knowledge reasoning in Large Language Models (LLMs), an area where such models frequently encounter difficulties. These difficulties often result in the generation of misleading or incorrect information, primarily due to their limited capacity to process evolving factual knowledge and complex temporal logic. In response, we propose a novel, constructivism-based approach that advocates for a paradigm shift in LLM learning towards an active, ongoing process of knowledge synthesis and customization. At the heart of our proposal is the Abstract Reasoning Induction ARI framework, which divides temporal reasoning into two distinct phases: Knowledge-agnostic and Knowledge-based. This division aims to reduce instances of hallucinations and improve LLMs' capacity for integrating abstract methodologies derived from historical data. Our approach achieves remarkable improvements, with relative gains of 29.7\% and 9.27\% on two temporal QA datasets, underscoring its efficacy in advancing temporal reasoning in LLMs. The code will be released at https://github.com/czy1999/ARI.

翻译：在本文中，我们应对大语言模型（LLMs）在时间知识推理中面临的重大挑战，这一领域中的模型常遭遇困难。这些困难往往导致生成误导性或错误的信息，主要源于它们处理不断演变的事实知识与复杂时间逻辑的能力有限。为此，我们提出了一种基于建构主义的新型方法，倡导LLM学习范式向主动、持续的知识综合与定制过程转变。我们方案的核心是抽象推理归纳（ARI）框架，该框架将时间推理划分为两个不同阶段：知识无关阶段与知识依赖阶段。这种划分旨在减少幻觉现象的发生，并提升LLM整合源于历史数据的抽象方法论的能力。我们的方法在两项时间问答数据集上取得了显著改进，相对提升分别达到29.7%和9.27%，彰显了其在推进LLM时间推理能力方面的有效性。代码将在https://github.com/czy1999/ARI发布。