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Despite their impressive capabilities, large pre-trained language models (LMs) struggle with consistent reasoning; recently, prompting LMs to generate explanations that self-guide the inference has emerged as a promising direction to amend this.
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Abductive commonsense reasoning
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Towards a human-like open-domain chatbot
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Wt5?! training text-to-text models to explain their predictions
Sharan Narang, Colin Raffel, Katherine Lee, Adam Roberts, Noah Fiedel, and Karishma Malkan. 2020 · 2004
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Robert G. M. Hausmann and Kurt VanLehn. 2007 · 2007
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Klaus Krippendorff. 2007 · 2007
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Core-guided maxsat with soft cardinality constraints
António Morgado, Carmine Dodaro, and Joao Marques-Silva. 2014 · 2014
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e-snli: Natural language inference with natural language explanations
Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, and Phil Blunsom. 2018 · 2018
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Adversarially regularising neural nli models to integrate logical background knowledge
Pasquale Minervini and Sebastian Riedel. 2018 · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
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Neural symbolic reader: Scalable integration of distributed and symbolic representations for reading comprehension
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Techniques for interpretable machine learning
Mengnan Du, Ninghao Liu, and Xia Hu. 2019 · 2019
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The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2019 · 2019
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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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What if we simply swap the two text fragments? a straightforward yet effective way to test the robustness of methods to confounding signals in nature language inference tasks
Haohan Wang, Da Sun, and Eric P Xing. 2019 · 2019
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Negated and misprimed probes for pretrained language models: Birds can talk, but cannot fly
Nora Kassner and Hinrich Schütze. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
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Generated knowledge prompting for commonsense reasoning
Jiacheng Liu, Alisa Liu, Ximing Lu, Sean Welleck, Peter West, Ronan Le Bras, Yejin Choi, and Hannaneh Hajishirzi. 2021 · 2021
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Unicorn on rainbow: A universal commonsense reasoning model on a new multitask benchmark
Nicholas Lourie, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2021 · 2021
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Neurologic decoding:(un) supervised neural text generation with predicate logic constraints
Ximing Lu, Peter West, Rowan Zellers, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2021a · 2021
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Creak: A dataset for commonsense reasoning over entity knowledge
Yasumasa Onoe, Michael J.Q. Zhang, Eunsol Choi, and Greg Durrett. 2021 · 2021
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COM2SENSE: A commonsense reasoning benchmark with complementary sentences
Shikhar Singh, Nuan Wen, Yu Hou, Pegah Alipoormolabashi, Te-lin Wu, Xuezhe Ma, and Nanyun Peng. 2021 · 2021
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Unsupervised commonsense question answering with self-talk
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Consistency of a recurrent language model with respect to incomplete decoding
Sean Welleck, Ilia Kulikov, Jaedeok Kim, Richard Yuanzhe Pang, and Kyunghyun Cho. 2020 · 2020
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Flexible generation of natural language deductions
Kaj Bostrom, Xinyu Zhao, Swarat Chaudhuri, and Greg Durrett. 2021 · 2021
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Learning to rationalize for nonmonotonic reasoning with distant supervision
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Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. 2021 · 2021
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Measuring and improving consistency in pretrained language models
Yanai Elazar, Nora Kassner, Shauli Ravfogel, Abhilasha Ravichander, Eduard Hovy, Hinrich Schütze, and Yoav Goldberg. 2021 · 2021
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Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies
Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot, Dan Roth, and Jonathan Berant. 2021 · 2021
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CommonsenseQA 2.0: Exposing the limits of AI through gamification
Alon Talmor, Ori Yoran, Ronan Le Bras, Chandra Bhagavatula, Yoav Goldberg, Yejin Choi, and Jonathan Berant. 2021 · 2021
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Teach me to explain: A review of datasets for explainable nlp
Sarah Wiegreffe and Ana Marasović. 2021 · 2021
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Calibrate before use: Improving few-shot performance of language models
Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021 · 2021
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Can rationalization improve robustness?
Howard Chen, Jacqueline He, Karthik Narasimhan, and Danqi Chen. 2022 · 2022
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Towards teachable reasoning systems
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Can language models learn from explanations in context?
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Self-consistency improves chain of thought reasoning in language models
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Chain of thought prompting elicits reasoning in large language models
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The unreliability of explanations in few-shot in-context learning
Xi Ye and Greg Durrett. 2022 · 2022
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Maximum satisfiability problem , pages 2035–2041. Springer US, Boston, MA
Roberto Battiti. 2009 · 2041
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