Towards Robust Temporal Reasoning of Large Language Models via a Multi-Hop QA Dataset and Pseudo-Instruction Tuning (2024.findings-acl)
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| Challenge: | Existing LLMs lack the ability to deal with temporal knowledge. |
| Approach: | They propose a temporal question-answering dataset Complex-TR that focuses on multi-answered and multi-hop temporal reasoning and propose augmentation strategy to improve LLMs' performance. |
| Outcome: | The proposed dataset improves LLMs’ performance on temporal QA benchmarks by significant margins. |
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Yimin Deng, Yejing Wang, Zhenxi Lin, Zichuan Fu, Guoshuai Zhao, Derong Xu, Yefeng Zheng, Xiangyu Zhao, Xian Wu, Li Zhu, Xueming Qian
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