Thought-Path Contrastive Learning via Premise-Oriented Data Augmentation for Logical Reading Comprehension

Roberta: A robustly optimized bert pretraining approach

Original

Liu, Y.; Ott, M.; Goyal, N.; Du, J.; Joshi, M.; Chen, D.; Levy, O.; Lewis, M.; Zettlemoyer, L.; and Stoyanov, V. 2019 · 1907

Earlier work this paper cites.

On the measure of intelligence

Original

Chollet, F. 2019 · 1911

Earlier work this paper cites.

Rank analysis of incomplete block designs: I. The method of paired comparisons

Bradley, R. A.; and Terry, M. E. 1952 · 1952

Earlier work this paper cites.

Reclor: A reading comprehension dataset requiring logical reasoning

Original

Yu, W.; Jiang, Z.; Dong, Y.; and Feng, J. 2020 · 2002

Earlier work this paper cites.

Logiqa: A challenge dataset for machine reading comprehension with logical reasoning

Original

Liu, J.; Cui, L.; Liu, H.; Huang, D.; Wang, Y.; and Zhang, Y. 2020 · 2007

Earlier work this paper cites.

Abstract meaning representation for sembanking

Banarescu, L.; Bonial, C.; Cai, S.; Georgescu, M.; Griffitt, K.; Hermjakob, U.; Knight, K.; Koehn, P.; Palmer, M.; and Schneider, N. 2013 · 2013

Earlier work this paper cites.

Decoupled weight decay regularization

Original

Loshchilov, I.; and Hutter, F. 2017 · 2017

Earlier work this paper cites.

Lora: Low-rank adaptation of large language models

Original

Hu, E. J.; Shen, Y.; Wallis, P.; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; and Chen, W. 2021 · 2021

Earlier work this paper cites.

DAGN: Discourse-Aware Graph Network for Logical Reasoning

Huang, Y.; Fang, M.; Cao, Y.; Wang, L.; and Liang, X. 2021 · 2021

Cited alongside, same era.

Large language models are zero-shot reasoners

Kojima, T.; Gu, S. S.; Reid, M.; Matsuo, Y.; and Iwasawa, Y. 2022 · 2022

Cited alongside, same era.

Training language models to follow instructions with human feedback

Ouyang, L.; Wu, J.; Jiang, X.; Almeida, D.; Wainwright, C.; Mishkin, P.; Zhang, C.; Agarwal, S.; Slama, K.; Ray, A.; et al. 2022 · 2022

Cited alongside, same era.

Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Text

Wang, S.; Zhong, W.; Tang, D.; Wei, Z.; Fan, Z.; Jiang, D.; Zhou, M.; and Duan, N. 2022 · 2022

Cited alongside, same era.

Automatic chain of thought prompting in large language models

Original

Zhang, Z.; Zhang, A.; Li, M.; and Smola, A. 2022 · 2022

Cited alongside, same era.

LogiCoT: Logical Chain-of-Thought Instruction Tuning

Liu, H.; Teng, Z.; Cui, L.; Zhang, C.; Zhou, Q.; and Zhang, Y. 2023b · 2023

Later among the works it cites.

LogiCoT: Logical Chain-of-Thought Instruction Tuning

Liu, H.; Teng, Z.; Cui, L.; Zhang, C.; Zhou, Q.; and Zhang, Y. 2023c · 2023

Later among the works it cites.

Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Rafailov, R.; Sharma, A.; Mitchell, E.; Manning, C. D.; Ermon, S.; and Finn, C. 2023 · 2023

Later among the works it cites.

Llama 2: Open foundation and fine-tuned chat models

Original

Touvron, H.; Martin, L.; Stone, K.; Albert, P.; Almahairi, A.; Babaei, Y.; Bashlykov, N.; Batra, S.; Bhargava, P.; Bhosale, S.; et al. 2023 · 2023

Later among the works it cites.

Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMs

Wang, H.; Wang, R.; Mi, F.; Deng, Y.; Wang, Z.; Liang, B.; Xu, R.; and Wong, K.-F. 2023 · 2023

Later among the works it cites.