In this study, we address the challenge of enhancing temporal knowledge reasoning in Large Language Models (LLMs). LLMs often struggle with this task, leading to the generation of inaccurate or misleading responses. This issue mainly arises from their limited ability to handle evolving factual knowledge and complex temporal logic. To overcome these limitations, we propose Abstract Reasoning Induction (ARI) framework, which divides temporal reasoning into two distinct phases: Knowledge-agnostic and Knowledge-based. This framework offers factual knowledge support to LLMs while minimizing the incorporation of extraneous noisy data. Concurrently, informed by the principles of constructivism, ARI provides LLMs the capability to engage in proactive, self-directed learning from both correct and incorrect historical reasoning samples. By teaching LLMs to actively construct knowledge and methods, it can significantly boosting their temporal reasoning abilities. 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 can be found at https://github.com/czy1999/ARI-QA

翻译：本研究致力于解决大语言模型在时序知识推理中面临的挑战。大语言模型在此类任务中常表现不佳，衍生出错误或误导性回答，其根源主要在于处理动态变化的事实知识与复杂时序逻辑的能力不足。为突破这些局限，我们提出抽象推理归纳框架，将时序推理划分为知识无关与知识依赖两个明确阶段。该框架在最大限度减少冗余噪声数据掺杂的同时，为大语言模型提供事实知识支撑；同时，依据建构主义理论原理，该框架赋予大语言模型从正确与错误历史推理样本中进行主动式自主学习的能力。通过引导大语言模型自主构建知识与方法，可显著提升其时序推理能力。在两个时序问答数据集上，本方法分别取得29.7%与9.27%的相对性能提升，充分验证了其在推进大语言模型时序推理方面的有效性。相关代码已发布于https://github.com/czy1999/ARI-QA