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Combining large language models with logical reasoning enhances their capacity to address problems in a robust and reliable manner.
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Logic-driven context extension and data augmentation for logical reasoning of text
Siyuan Wang, Wanjun Zhong, Duyu Tang, Zhongyu Wei, Zhihao Fan, Daxin Jiang, Ming Zhou, and Nan Duan. 2022 · 2022
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Chain-of-thought prompting elicits reasoning in large language models
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Qiming Bao, Gael Gendron, Alex Yuxuan Peng, Wanjun Zhong, Neset Tan, Yang Chen, Michael Witbrock, and Jiamou Liu. 2023 · 2023
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Large language models are not strong abstract reasoners
Gaël Gendron, Qiming Bao, Michael Witbrock, and Gillian Dobbie. 2023 · 2023
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Logiqa 2.0—an improved dataset for logical reasoning in natural language understanding
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Logiqa: A challenge dataset for machine reading comprehension with logical reasoning
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Multi-step deductive reasoning over natural language: An empirical study on out-of-distribution generalisation
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Enhancing data augmentation with knowledge-enriched data generation via dynamic prompt-tuning method
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Developing And Assessing Language Models For Logical Reasoning Over Natural Language
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Exploring iterative enhancement for improving learnersourced multiple-choice question explanations with large language models
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