Large Language Models Can Learn Temporal Reasoning

Joint reasoning for temporal and causal relations

Original

Qiang Ning, Zhili Feng, Hao Wu, and Dan Roth. 2019 · 1906

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"going on a vacation" takes longer than" going for a walk": A study of temporal commonsense understanding

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Back to the future: Towards explainable temporal reasoning with large language models

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Temporal common sense acquisition with minimal supervision

Original

Ben Zhou, Qiang Ning, Daniel Khashabi, and Dan Roth. 2020a · 2005

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Temporal reasoning on implicit events from distant supervision

Original

Ben Zhou, Kyle Richardson, Qiang Ning, Tushar Khot, Ashish Sabharwal, and Dan Roth. 2020b · 2010

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Hyte: Hyperplane-based temporally aware knowledge graph embedding

Shib Sankar Dasgupta, Swayambhu Nath Ray, and Partha Talukdar. 2018 · 2011

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Improving event duration prediction via time-aware pre-training

Original

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Learning sequence encoders for temporal knowledge graph completion

Alberto García-Durán, Sebastijan Dumančić, and Mathias Niepert. 2018 · 2018

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Retrieval-augmented generation for knowledge-intensive nlp tasks

Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. 2020 · 2020

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Temporal reasoning in natural language inference

Siddharth Vashishtha, Adam Poliak, Yash Kumar Lal, Benjamin Van Durme, and Aaron Steven White. 2020 · 2020

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A dataset for answering time-sensitive questions

Original

Wenhu Chen, Xinyi Wang, and William Yang Wang. 2021 · 2021

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Lora: Low-rank adaptation of large language models

Original

Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021 · 2021

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Show your work: Scratchpads for intermediate computation with language models

Original

Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, et al. 2021 · 2021

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Timedial: Temporal commonsense reasoning in dialog

Original

Lianhui Qin, Aditya Gupta, Shyam Upadhyay, Luheng He, Yejin Choi, and Manaal Faruqui. 2021 · 2021

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Large language models are reasoning teachers

Original

Namgyu Ho, Laura Schmid, and Se-Young Yun. 2022 · 2022

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Towards reasoning in large language models: A survey

Original

Jie Huang and Kevin Chen-Chuan Chang. 2022 · 2022

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Large language models are zero-shot reasoners

Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022

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Training language models to follow instructions with human feedback

Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022 · 2022

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Attention-focused adversarial training for robust temporal reasoning

Lis Kanashiro Pereira. 2022 · 2022

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Time masking for temporal language models

Guy D Rosin, Ido Guy, and Kira Radinsky. 2022 · 2022

Cited alongside, same era.

Language models are greedy reasoners: A systematic formal analysis of chain-of-thought

Original

Abulhair Saparov and He He. 2022 · 2022

Cited alongside, same era.

Chain-of-thought prompting elicits reasoning in large language models

Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022

Cited alongside, same era.

Temporal inductive logic reasoning

Original

Yuan Yang, Siheng Xiong, James C Kerce, and Faramarz Fekri. 2022 · 2022

Cited alongside, same era.

Gpt-4 technical report

Original

Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023 · 2023

Large language models can learn rules

Original

Zhaocheng Zhu, Yuan Xue, Xinyun Chen, Denny Zhou, Jian Tang, Dale Schuurmans, and Hanjun Dai. 2023 · 2023

Later among the works it cites.

Self-improvement programming for temporal knowledge graph question answering

Zhuo Chen, Zhao Zhang, Zixuan Li, Fei Wang, Yutao Zeng, Xiaolong Jin, and Yongjun Xu. 2024b · 2024

Closest in time.

Call me when necessary: Llms can efficiently and faithfully reason over structured environments

Original

Sitao Cheng, Ziyuan Zhuang, Yong Xu, Fangkai Yang, Chaoyun Zhang, Xiaoting Qin, Xiang Huang, Ling Chen, Qingwei Lin, Dongmei Zhang, et al. 2024 · 2024

Closest in time.

Graph machine learning in the era of large language models (llms)

Original

Wenqi Fan, Shijie Wang, Jiani Huang, Zhikai Chen, Yu Song, Wenzhuo Tang, Haitao Mao, Hui Liu, Xiaorui Liu, Dawei Yin, et al. 2024 · 2024

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