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Knowledge in the real world is being updated constantly. However, it iscostly to frequently update large language models (LLMs). Therefore, it iscrucial for LLMs to understand the concept of temporal knowledge. However,prior works on temporal question answering did not emphasize multi-answer andmulti-hop types of temporal reasoning. In this paper, we propose a complextemporal question-answering (QA) dataset Complex-TR that focuses onmulti-answer and multi-hop temporal reasoning. Besides, we also propose a noveldata augmentation strategy to improve the complex temporal reasoning capabilityand robustness of LLMs. We conducted experiments on multiple temporal QAdatasets. Experimental results show that our method is able to improve LLMs'performance on temporal QA benchmarks by significant margins.

Towards Robust Temporal Reasoning of Large Language Models via a Multi-Hop QA Dataset and Pseudo-Instruction Tuning