Towards Robust Temporal Reasoning of Large Language Models via a
  Multi-Hop QA Dataset and Pseudo-Instruction Tuning
Qingyu Tan Hwee Tou Ng Lidong Bing
Knowledge in the real world is being updated constantly. However, it is
costly to frequently update large language models (LLMs). Therefore, it is
crucial for LLMs to understand the concept of temporal knowledge. However,
prior works on temporal question answering did not emphasize multi-answer and
multi-hop types of temporal reasoning. In this paper, we propose a complex
temporal question-answering (QA) dataset Complex-TR that focuses on
multi-answer and multi-hop temporal reasoning. Besides, we also propose a novel
data augmentation strategy to improve the complex temporal reasoning capability
and robustness of LLMs. We conducted experiments on multiple temporal QA
datasets. Experimental results show that our method is able to improve LLMs'
performance on temporal QA benchmarks by significant margins.