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Counterfactual data augmentation (CDA) -- i.e., adding minimally perturbed inputs during training -- helps reduce model reliance on spurious correlations and improves generalization to out-of-distribution (OOD) data.
Quantifying the carbon emissions of machine learning
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REALM: Retrieval-augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Ming-Wei Chang. 2020 · 2002
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Video google: a text retrieval approach to object matching in videos
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Language models are few-shot learners
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Twitter sentiment classification using distant supervision
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Measuring and reducing gendered correlations in pre-trained models
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Learning word vectors for sentiment analysis
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Hidden factors and hidden topics: Understanding rating dimensions with review text
Julian McAuley and Jure Leskovec. 2013 · 2013
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts. 2013 · 2013
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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
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A large annotated corpus for learning natural language inference
Bowman, Samuel R. and Angeli, Gabor and Potts, Christopher, and Manning, Christopher D. 2015 · 2015
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Yelp dataset challenge: Review rating prediction
Nabiha Asghar. 2016 · 2016
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Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. 2016 · 2016
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SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Breaking NLI systems with sentences that require simple lexical inferences
Max Glockner, Vered Shwartz, and Yoav Goldberg. 2018 · 2018
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Search engine guided neural machine translation
Jiatao Gu, Yong Wang, Kyunghyun Cho, and Victor OK Li. 2018 · 2018
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Stress test evaluation for natural language inference
Aakanksha Naik, Abhilasha Ravichander, Norman Sadeh, Carolyn Rose, and Graham Neubig. 2018 · 2018
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2018 · 2018
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Retrieve and refine: Improved sequence generation models for dialogue
Jason Weston, Emily Dinan, and Alexander Miller. 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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Texygen: A benchmarking platform for text generation models
Yaoming Zhu, Sidi Lu, Lei Zheng, Jiaxian Guo, Weinan Zhang, Jun Wang, and Yong Yu. 2018 · 2018
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Controllable paraphrase generation with a syntactic exemplar
Mingda Chen, Qingming Tang, Sam Wiseman, and Kevin Gimpel. 2019 · 2019
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Billion-scale similarity search with gpus
Jeff Johnson, Matthijs Douze, and Hervé Jégou. 2019 · 2019
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Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
Tom McCoy, Ellie Pavlick, and Tal Linzen. 2019 · 2019
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Logic-guided data augmentation and regularization for consistent question answering
Akari Asai and Hannaneh Hajishirzi. 2020 · 2020
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ERASER: A benchmark to evaluate rationalized NLP models
Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, Eric Lehman, Caiming Xiong, Richard Socher, and Byron C. Wallace. 2020 · 2020
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Evaluating models’ local decision boundaries via contrast sets
Deberta: Decoding-enhanced bert with disentangled attention
Pengcheng He, Xiaodong Liu, Jianfeng Gao, and Weizhu Chen. 2021 · 2021
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On the efficacy of adversarial data collection for question answering: Results from a large-scale randomized study
Divyansh Kaushik, Douwe Kiela, Zachary C. Lipton, and Wen-tau Yih. 2021 · 2021
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Sparse, dense, and attentional representations for text retrieval
Yi Luan, Jacob Eisenstein, Kristina Toutanova, and Michael Collins. 2021 · 2021
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Generate your counterfactuals: Towards controlled counterfactual generation for text
Nishtha Madaan, Inkit Padhi, Naveen Panwar, and Diptikalyan Saha. 2021 · 2021
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A recipe for arbitrary text style transfer with large language models
Emily Reif, Daphne Ippolito, Ann Yuan, Andy Coenen, Chris Callison-Burch, and Jason Wei. 2021 · 2021
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Matt Gardner, Yoav Artzi, Victoria Basmov, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, Ananth Gottumukkala, Nitish Gupta, Hannaneh Hajishirzi, Gabriel Ilharco, Daniel Khashabi, Kevin Lin, Jiangming Liu, Nelson F. Liu, Phoebe Mulcaire, Qiang Ning, Sameer Singh, Noah A. Smith, Sanjay Subramanian, Reut Tsarfaty, Eric Wallace, Ally Zhang, and Ben Zhou. 2020 · 2020
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Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020 · 2020
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Learning the difference that makes a difference with counterfactually augmented data
Divyansh Kaushik, Eduard Hovy, and Zachary C Lipton. 2020 · 2020
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More bang for your buck: Natural perturbation for robust question answering
Daniel Khashabi, Tushar Khot, and Ashish Sabharwal. 2020 · 2020
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Reformulating unsupervised style transfer as paraphrase generation
Kalpesh Krishna, John Wieting, and Mohit Iyyer. 2020 · 2020
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Contrastive representation learning: A framework and review
Phuc H Le-Khac, Graham Healy, and Alan F Smeaton. 2020 · 2020
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. 2020 · 2020
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Explaining NLP models via minimal contrastive editing (MiCE)
Alexis Ross, Ana Marasović, and Matthew Peters. 2021 · 2021
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Augmented SBERT: Data augmentation method for improving bi-encoders for pairwise sentence scoring tasks
Nandan Thakur, Nils Reimers, Johannes Daxenberger, and Iryna Gurevych. 2021 · 2021
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Measure and improve robustness in nlp models: A survey
Xuezhi Wang, Haohan Wang, and Diyi Yang. 2021 · 2021
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Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. 2021 · 2021
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Polyjuice: Generating counterfactuals for explaining, evaluating, and improving models
Tongshuang Wu, Marco Tulio Ribeiro, Jeffrey Heer, and Daniel Weld. 2021 · 2021
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Transductive learning for unsupervised text style transfer
Fei Xiao, Liang Pang, Yanyan Lan, Yan Wang, Huawei Shen, and Xueqi Cheng. 2021 · 2021
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CRASS: A novel data set and benchmark to test counterfactual reasoning of large language models
Jörg Frohberg and Frank Binder. 2022 · 2022
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An investigation of the (in)effectiveness of counterfactually augmented data
Nitish Joshi and He He. 2022 · 2022
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Wanli: Worker and ai collaboration for natural language inference dataset creation
Alisa Liu, Swabha Swayamdipta, Noah A Smith, and Yejin Choi. 2022 · 2022
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Retrieval-guided counterfactual generation for QA
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Cold decoding: Energy-based constrained text generation with langevin dynamics
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A recipe for arbitrary text style transfer with large language models
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Tailor: Generating and perturbing text with semantic controls
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Chain of thought prompting elicits reasoning in large language models
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Generating data to mitigate spurious correlations in natural language inference datasets
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Least-to-most prompting enables complex reasoning in large language models
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