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While pretrained language models achieve excellent performance on natural language understanding benchmarks, they tend to rely on spurious correlations and generalize poorly to out-of-distribution (OOD) data.
RoBERTa: A robustly optimized BERT pretraining approach
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Jason Wei and Kai Zou. 2019 · 2019
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Ran Zmigrod, Sabrina J. Mielke, Hanna Wallach, and Ryan Cotterell. 2019 · 2019
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Good-enough compositional data augmentation
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Evaluating NLP models via contrast sets
M. Gardner, Y. Artzi, V. Basmova, J. Berant, B. Bogin, S. Chen, P. Dasigi, D. Dua, Y. Elazar, A. Gottumukkala, N. Gupta, H. Hajishirzi, G. Ilharco, D. Khashabi, K. Lin, J. Liu, N. F. Liu, P. Mulcaire, Q. Ning, S. Singh, N. A. Smith, S. Subramanian, R. Tsarfaty, E. Wallace, A. Zhang, and B. Zhou. 2020 · 2020
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Counterfactually-augmented SNLI training data does not yield better generalization than unaugmented data
William Huang, Haokun Liu, and Samuel R. Bowman. 2020 · 2020
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Gender bias in neural natural language processing
Kaiji Lu, Piotr Mardziel, Fangjing Wu, Preetam Amancharla, and A. Datta. 2020 · 2020
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Divyansh Kaushik, Amrith Setlur, Eduard H Hovy, and Zachary Chase Lipton. 2021 · 2021
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