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Language models have been shown to perform remarkably well on a wide range of natural language processing tasks.
Roberta: A robustly optimized bert pretraining approach
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Constructing Datasets for Multi-hop Reading Comprehension Across Documents
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HotpotQA: A dataset for diverse, explainable multi-hop question answering
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Understanding dataset design choices for multi-hop reasoning
Jifan Chen and Greg Durrett. 2019 · 2019
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Compositional questions do not necessitate multi-hop reasoning
Sewon Min, Eric Wallace, Sameer Singh, Matt Gardner, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2019 · 2019
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Pytorch: An imperative style, high-performance deep learning library
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Explaining answers with entailment trees
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Text modular networks: Learning to decompose tasks in the language of existing models
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The power of scale for parameter-efficient prompt tuning
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Explain yourself! leveraging language models for commonsense reasoning
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher. 2019 · 2019
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NLProlog: Reasoning with weak unification for question answering in natural language
Leon Weber, Pasquale Minervini, Jannes Münchmeyer, Ulf Leser, and Tim Rocktäschel. 2019 · 2019
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Rethinking cooperative rationalization: Introspective extraction and complement control
Mo Yu, Shiyu Chang, Yang Zhang, and Tommi Jaakkola. 2019 · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 2020
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Transformers as soft reasoners over language
Peter Clark, Oyvind Tafjord, and Kyle Richardson. 2020 · 2020
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MonaLog: a lightweight system for natural language inference based on monotonicity
Hai Hu, Qi Chen, Kyle Richardson, Atreyee Mukherjee, Lawrence S. Moss, and Sandra Kuebler. 2020 · 2020
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Qasc: A dataset for question answering via sentence composition
Tushar Khot, Peter Clark, Michal Guerquin, Peter Jansen, and Ashish Sabharwal. 2020 · 2020
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Generating predicate logic expressions from natural language
Oleksii Levkovskyi and Wei Li. 2021 · 2021
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Maxwell Nye, Michael Tessler, Josh Tenenbaum, and Brenden M Lake. 2021 · 2021
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Neural unification for logic reasoning over natural language
Gabriele Picco, Hoang Thanh Lam, Marco Luca Sbodio, and Vanessa Lopez Garcia. 2021 · 2021
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ProofWriter: Generating implications, proofs, and abductive statements over natural language
Oyvind Tafjord, Bhavana Dalvi, and Peter Clark. 2021 · 2021
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DeepA2: A modular framework for deep argument analysis with pretrained neural Text2Text language models
Gregor Betz and Kyle Richardson. 2022 · 2022
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Natural language deduction through search over statement compositions
Kaj Bostrom, Zayne Sprague, Swarat Chaudhuri, and Greg Durrett. 2022 · 2022
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RLET: A reinforcement learning based approach for explainable QA with entailment trees
Tengxiao Liu, Qipeng Guo, Xiangkun Hu, Yue Zhang, Xipeng Qiu, and Zheng Zhang. 2022 · 2022
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Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, and Denny Zhou. 2022 · 2022
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Dynamic generation of interpretable inference rules in a neuro-symbolic expert system
Nathaniel Weir and Benjamin Van Durme. 2022 · 2022
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Selection-inference: Exploiting large language models for interpretable logical reasoning
Antonia Creswell, Murray Shanahan, and Irina Higgins. 2023 · 2023
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Specializing smaller language models towards multi-step reasoning
Yao Fu, Hao Peng, Litu Ou, Ashish Sabharwal, and Tushar Khot. 2023 · 2023
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Language models are greedy reasoners: A systematic formal analysis of chain-of-thought
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On the paradox of learning to reason from data
Honghua Zhang, Liunian Harold Li, Tao Meng, Kai-Wei Chang, and Guy Van den Broeck. 2023 · 2023
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