Perceive the Passage of Time: A Systematic Evaluation of Large Language Model in Temporal Relativity

Shuang Chen, Yining Zheng, Shimin Li, Qinyuan Cheng, Xipeng Qiu

Abstract: Temporal perception is crucial for Large Language Models(LLMs) to effectively understand the world. However, current benchmarks primarily focus on temporal reasoning, falling short in understanding the temporal characteristics involving temporal perception, particularly in understanding temporal relativity. In this paper, we introduce TempBench, a comprehensive benchmark designed to evaluate the temporal-relative ability of LLMs. TempBench encompasses 4 distinct scenarios: Physiology, Psychology, Cognition and Mixture. We conduct an extensive experiments on GPT-4, a series of Llama and other popular LLMs. The experiment results demonstrate a significant performance gap between LLMs and humans in temporal-relative capability. Furthermore, the error types of temporal-relative ability in LLMs are proposed to thoroughly analyze the impact of multiple aspects and emphasize the associated challenges. We anticipate that TempBench will drive further advancements in enhancing the temporal-perceiving capabilities of L