The development of Generative Pre-trained Transformer (GPT) models has marked a significant milestone in the field of natural language processing (NLP). These models, with their ability to generate coherent and contextually relevant text, have found applications in diverse areas such as chatbots, automated content creation, and language translation. One crucial aspect of NLP where GPT models are evaluated is temporal reasoning. Temporal reasoning involves understanding, representing, and manipulating temporal information in natural language. This paper explores the capabilities of GPT models in temporal reasoning, highlighting their strengths and identifying areas where they may fail. Through various examples, we aim to provide a comprehensive understanding of the current state and potential future directions for improving temporal reasoning in GPT models.
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Deroy et al. (2024) studied this question.
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