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Our goal is a teachable reasoning system for question-answering (QA), where a user can interact with faithful answer explanations, and correct its errors so that the system improves over time.
QuaRTz: An open-domain dataset of qualitative relationship questions
Oyvind Tafjord, Matt Gardner, Kevin Lin, and Peter Clark. 2019 · 1909
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Interactive transfer of expertise: Acquisition of new inference rules
Randall Davis. 1977 · 1977
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Convince: A conversational inference consolidation engine
Jin H. Kim and Judea Pearl. 1987 · 1987
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Toward an architecture for never-ending language learning
Andrew Carlson, J. Betteridge, Bryan Kisiel, Burr Settles, Estevam R. Hruschka, and Tom Michael Mitchell. 2010 · 2010
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Learning knowledge graphs for question answering through conversational dialog
Ben Hixon, Peter Clark, and Hannaneh Hajishirzi. 2015 · 2015
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Transforming question answering datasets into natural language inference datasets
Dorottya Demszky, Kelvin Guu, and Percy Liang. 2018 · 2018
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Can a suit of armor conduct electricity? A new dataset for open book question answering
Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal. 2018 · 2018
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Recommendation as a communication game: Self-supervised bot-play for goal-oriented dialogue
Dongyeop Kang, Anusha Balakrishnan, Pararth Shah, Paul A. Crook, Y-Lan Boureau, and Jason Weston. 2019 · 2019
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Learning to attend on essential terms: An enhanced retriever-reader model for open-domain question answering
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Continual lifelong learning with neural networks: A review
G. I. Parisi, Ronald Kemker, Jose L. Part, Christopher Kanan, and S. Wermter. 2019 · 2019
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Explanatory interactive machine learning
Stefano Teso and Kristian Kersting. 2019 · 2019
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Language models are few-shot learners
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From ’f’ to ’a’ on the n.y. regents science exams: An overview of the aristo project
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Learning the difference that makes a difference with counterfactually-augmented data
Divyansh Kaushik, Eduard H. Hovy, and Zachary Chase Lipton. 2020 · 2020
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Interactive task learning from GUI-grounded natural language instructions and demonstrations
Toby Jia-Jun Li, Tom Michael Mitchell, and Brad A. Myers. 2020 · 2020
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Explaining answers with entailment trees
Bhavana Dalvi, Peter A. Jansen, Oyvind Tafjord, Zhengnan Xie, Hannah Smith, Leighanna Pipatanangkura, and Peter Clark. 2021 · 2021
Eric Mitchell, Charles Lin, Antoine Bosselut, Chelsea Finn, and Christopher D. Manning. 2021 · 2021
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Learning to retrieve prompts for in-context learning
Ohad Rubin, Jonathan Herzig, and Jonathan Berant. 2021 · 2021
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General-purpose question-answering with Macaw
Oyvind Tafjord and Peter Clark. 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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Rethinking explainability as a dialogue: A practitioner’s perspective
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Editing factual knowledge in language models
Nicola De Cao, Wilker Aziz, and Ivan Titov. 2021 · 2021
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Do language models have beliefs? methods for detecting, updating, and visualizing model beliefs
Peter Hase, Mona Diab, Asli Celikyilmaz, Xian Li, Zornitsa Kozareva, Veselin Stoyanov, Mohit Bansal, and Srinivasan Iyer. 2021 · 2021
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BeliefBank: Adding memory to a pre-trained language model for a systematic notion of belief
Nora Kassner, Oyvind Tafjord, Hinrich Schutze, and Peter Clark. 2021 · 2021
Cited alongside, same era.
Self-supervised bot play for conversational recommendation with justifications
Shuyang Li, Bodhisattwa Prasad Majumder, and Julian McAuley. 2021 · 2021
Cited alongside, same era.
Learning to repair: Repairing model output errors after deployment using a dynamic memory of feedback
Niket Tandon, Aman Madaan, Peter Clark, and Yiming Yang. 2022a
Cited in the paper.
Memory-assisted prompt editing to improve GPT-3 after deployment
Niket Tandon, Aman Madaan, Peter Clark, and Yiming Yang. 2022b
Cited in the paper.
Himabindu Lakkaraju, Dylan Slack, Yuxin Chen, Chenhao Tan, and Sameer Singh. 2022 · 2022
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A rationale-centric framework for human-in-the-loop machine learning
Jinghui Lu, Linyi Yang, Brian Namee, and Yue Zhang. 2022 · 2022
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Blenderbot 3: a deployed conversational agent that continually learns to responsibly engage
Kurt Shuster, Jing Xu, Mojtaba Komeili, Da Ju, Eric Michael Smith, Stephen Roller, Megan Ung, Moya Chen, Kushal Arora, Joshua Lane, Morteza Behrooz, William Ngan, Spencer Poff, Naman Goyal, Arthur Szlam, Y-Lan Boureau, Melanie Kambadur, and Jason Weston. 2022 · 2022
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Entailer: Answering questions with faithful and truthful chains of reasoning
Oyvind Tafjord, Bhavana Dalvi Mishra, and Peter Clark. 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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