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Neural language models (LMs) have achieved impressive results on various language-based reasoning tasks by utilizing latent knowledge encoded in their own pretrained parameters.
Extraction of salient sentences from labelled documents
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Open domain question answering using wikipedia-based knowledge model
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Reading Wikipedia to answer open-domain questions
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Towards a rigorous science of interpretable machine learning
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Axiomatic attribution for deep networks
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Annotation artifacts in natural language inference data
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The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery
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Towards explainable nlp: A generative explanation framework for text classification
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Can a suit of armor conduct electricity? a new dataset for open book question answering
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Commonsenseqa: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant · 2018
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COMET: Commonsense transformers for automatic knowledge graph construction
Antoine Bosselut, Hannah Rashkin, Maarten Sap, Chaitanya Malaviya, Asli Celikyilmaz, and Yejin Choi · 2019
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Friends don’t let friends deploy black-box models: The importance of intelligibility in machine learning
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Kagnet: Knowledge-aware graph networks for commonsense reasoning
Bill Yuchen Lin, Xinyue Chen, Jamin Chen, and Xiang Ren · 2019
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Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
R Thomas McCoy, Ellie Pavlick, and Tal Linzen · 2019
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Explain yourself! leveraging language models for commonsense reasoning
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin · 2019
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CommonsenseQA: A question answering challenge targeting commonsense knowledge
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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
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Underspecification presents challenges for credibility in modern machine learning
Alexander D’Amour, Katherine Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D Hoffman, et al · 2020
Explanations for commonsenseqa: New dataset and models
Shourya Aggarwal, Divyanshu Mandowara, Vishwajeet Agrawal, Dinesh Khandelwal, Parag Singla, and Dinesh Garg · 2021
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On the dangers of stochastic parrots: Can language models be too big?
Emily M Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell · 2021
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Shortcutted commonsense: Data spuriousness in deep learning of commonsense reasoning
Ruben Branco, António Branco, Joao Rodrigues, and Joao Silva · 2021
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Salkg: Learning from knowledge graph explanations for commonsense reasoning
Aaron Chan, Jiashu Xu, Boyuan Long, Soumya Sanyal, Tanishq Gupta, and Xiang Ren · 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
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Scalable multi-hop relational reasoning for knowledge-aware question answering
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Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A Wichmann · 2020
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Peter Hase, Shiyue Zhang, Harry Xie, and Mohit Bansal · 2020
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QASC: A dataset for question answering via sentence composition
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Nile: Natural language inference with faithful natural language explanations
Sawan Kumar and Partha Talukdar · 2020
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Graph-based reasoning over heterogeneous external knowledge for commonsense question answering
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Learning to deceive knowledge graph augmented models via targeted perturbation
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Reframing human-ai collaboration for generating free-text explanations
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Learning contextualized knowledge structures for commonsense reasoning
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GPT-NeoX-20B: An open-source autoregressive language model
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Explanations from large language models make small reasoners better
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Few-shot self-rationalization with natural language prompts
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