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Large language models (LMs) are capable of generating free-text rationales to aid question answering.
Extraction of salient sentences from labelled documents
Misha Denil, Alban Demiraj, and Nando De Freitas · 2014
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Visualizing and understanding neural models in nlp
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Rationalizing neural predictions
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Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, Caiming Xiong, and Richard Socher · 2019
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Quarel: A dataset and models for answering questions about qualitative relationships
Oyvind Tafjord, Peter Clark, Matt Gardner, Wen-tau Yih, and Ashish Sabharwal · 2019
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Attention is not not explanation
Sarah Wiegreffe and Yuval Pinter · 2019
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Language models are few-shot learners, 2020
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Peter Hase, Shiyue Zhang, Harry Xie, and Mohit Bansal · 2020
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Learning to faithfully rationalize by construction
Sarthak Jain, Sarah Wiegreffe, Yuval Pinter, and Byron C Wallace · 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
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Bill Yuchen Lin, Seyeon Lee, Rahul Khanna, and Xiang Ren · 2020
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The radicalization risks of gpt-3 and advanced neural language models
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Generalized entropy regularization or: There’s nothing special about label smoothing
Clara Meister, Elizabeth Salesky, and Ryan Cotterell · 2020
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Wt5?! training text-to-text models to explain their predictions
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Evaluating explanations: How much do explanations from the teacher aid students?
Danish Pruthi, Bhuwan Dhingra, Livio Baldini Soares, Michael Collins, Zachary C Lipton, Graham Neubig, and William W Cohen · 2020
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Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, and Denny Zhou · 2022
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Reframing human-AI collaboration for generating free-text explanations
Sarah Wiegreffe, Jack Hessel, Swabha Swayamdipta, Mark Riedl, and Yejin Choi · 2022
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Tailor: A prompt-based approach to attribute-based controlled text generation, 2022
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Faithfulness tests for natural language explanations
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Measuring association between labels and free-text rationales
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Kevin Yang and Dan Klein · 2021
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Training a helpful and harmless assistant with reinforcement learning from human feedback
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Unirex: A unified learning framework for language model rationale extraction
Aaron Chan, Maziar Sanjabi, Lambert Mathias, Liang Tan, Shaoliang Nie, Xiaochang Peng, Xiang Ren, and Hamed Firooz · 2022
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Scaling instruction-finetuned language models
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Knife: Distilling reasoning knowledge from free-text rationales, 2023
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REV: Information-theoretic evaluation of free-text rationales
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Are machine rationales (not) useful to humans? measuring and improving human utility of free-text rationales
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