Towards Robust Temporal Reasoning of Large Language Models via a Multi-Hop QA Dataset and Pseudo-Instruction Tuning
This work addresses the challenge of temporal knowledge understanding for LLMs, which is incremental as it builds on prior TQA efforts by focusing on multi-answer and multi-hop reasoning.
The paper tackles the problem of temporal reasoning in large language models by introducing a multi-hop QA dataset and a pseudo-instruction tuning method, resulting in significant performance improvements on temporal QA benchmarks.
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 (TQA) did not emphasize multi-answer and multi-hop types of temporal reasoning. In this paper, we propose a complex temporal question-answering 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. Our code and data are released at: https://github.com/nusnlp/complex-tr.