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Is it possible to use natural language to intervene in a model's behavior and alter its prediction in a desired way? We investigate the effectiveness of natural language interventions for reading-comprehension systems, studying this in the context of social stereotypes.
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Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
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Tao Li, Daniel Khashabi, Tushar Khot, Ashish Sabharwal, and Vivek Srikumar. 2020 · 2020
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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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Thinking like a skeptic: Defeasible inference in natural language
Rachel Rudinger, Vered Shwartz, Jena D Hwang, Chandra Bhagavatula, Maxwell Forbes, Ronan Le Bras, Noah A Smith, and Yejin Choi. 2020 · 2020
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Inoculation by fine-tuning: A method for analyzing challenge datasets
Nelson F Liu, Roy Schwartz, and Noah A Smith. 2019a
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RoBERTa: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar S. Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke S. Zettlemoyer, and Veselin Stoyanov. 2019b
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Natural instructions: Benchmarking generalization to new tasks from natural language instructions
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