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In this paper, we tackle the significant challenge of temporal knowledgereasoning in Large Language Models (LLMs), an area where such models frequentlyencounter difficulties. These difficulties often result in the generation ofmisleading or incorrect information, primarily due to their limited capacity toprocess evolving factual knowledge and complex temporal logic. In response, wepropose a novel, constructivism-based approach that advocates for a paradigmshift in LLM learning towards an active, ongoing process of knowledge synthesisand customization. At the heart of our proposal is the Abstract ReasoningInduction ARI framework, which divides temporal reasoning into two distinctphases: Knowledge-agnostic and Knowledge-based. This division aims to reduceinstances of hallucinations and improve LLMs' capacity for integrating abstractmethodologies derived from historical data. Our approach achieves remarkableimprovements, with relative gains of 29.7\% and 9.27\% on two temporal QAdatasets, underscoring its efficacy in advancing temporal reasoning in LLMs.The code will be released at https://github.com/czy1999/ARI.

Temporal Knowledge Question Answering via Abstract Reasoning Induction