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Large language models (LLMs) have achieved state-of-the-art performance on a series of natural language understanding tasks.
Axiomatic Attribution for Deep Networks
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Don’t Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases
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Bert: Pre-training of deep bidirectional transformers for language understanding
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How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) . 55–65
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Incorporating priors with feature attribution on text classification
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Roberta: A robustly optimized bert pretraining approach
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Probing neural network comprehension of natural language arguments
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Towards debiasing fact verification models
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What does BERT Learn from Multiple-Choice Reading Comprehension Datasets?
Chenglei Si, Shuohang Wang, Min-Yen Kan, and Jing Jiang. 2019 · 2019
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Language models are few-shot learners
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Fairness in deep learning: A computational perspective
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Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL)
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Is bert really robust? a strong baseline for natural language attack on text classification and entailment. In Proceedings of the AAAI conference on artificial intelligence (AAAI)
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Look at the First Sentence: Position Bias in Question Answering. In Empirical Methods in Natural Language Processing (EMNLP)
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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
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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What do Models Learn from Question Answering Datasets?. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Priyanka Sen and Amir Saffari. 2020 · 2020
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The pitfalls of simplicity bias in neural networks
Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, and Praneeth Netrapalli. 2020 · 2020
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Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training
Joe Stacey, Pasquale Minervini, Haim Dubossarsky, Sebastian Riedel, and Tim Rocktäschel. 2020 · 2020
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An embarrassingly simple approach for trojan attack in deep neural networks. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD)
MATE-KD: Masked Adversarial TExt, a Companion to Knowledge Distillation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL)
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Can contrastive learning avoid shortcut solutions?
Joshua Robinson, Li Sun, Ke Yu, Kayhan Batmanghelich, Stefanie Jegelka, and Suvrit Sra. 2021 · 2021
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Masked language modeling and the distributional hypothesis: Order word matters pre-training for little
Koustuv Sinha, Robin Jia, Dieuwke Hupkes, Joelle Pineau, Adina Williams, and Douwe Kiela. 2021 · 2021
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Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning
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Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models
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Ruixiang Tang, Mengnan Du, Ninghao Liu, Fan Yang, and Xia Hu. 2020 · 2020
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Unshuffling data for improved generalization
Damien Teney, Ehsan Abbasnejad, and Anton van den Hengel. 2020 · 2020
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An empirical study on robustness to spurious correlations using pre-trained language models
Lifu Tu, Garima Lalwani, Spandana Gella, and He He. 2020 · 2020
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Towards debiasing NLU models from unknown biases
Prasetya Ajie Utama, Nafise Sadat Moosavi, and Iryna Gurevych. 2020 · 2020
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Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics. In Proceedings of the Second Workshop on Insights from Negative Results in NLP
Prajjwal Bhargava, Aleksandr Drozd, and Anna Rogers. 2021 · 2021
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Shortcutted Commonsense: Data Spuriousness in Deep Learning of Commonsense Reasoning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Ruben Branco, António Branco, João Silva, and João Rodrigues. 2021 · 2021
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A universal law of robustness via isoperimetry
Sébastien Bubeck and Mark Sellke. 2021 · 2021
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Towards Interpreting and Mitigating Shortcut Learning Behavior of NLU Models
Mengnan Du, Varun Manjunatha, Rajiv Jain, Ruchi Deshpande, Franck Dernoncourt, Jiuxiang Gu, Tong Sun, and Xia Hu. 2021 · 2021
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Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021 · 2021
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C2l: Causally contrastive learning for robust text classification. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 36. 10526–10534
Seungtaek Choi, Myeongho Jeong, Hojae Han, and Seung-won Hwang. 2022 · 2022
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Show Your Work: Scratchpads for Intermediate Computation with Language Models
Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, et al · 2022
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Gradient matching for domain generalization
Yuge Shi, Jeffrey Seely, Philip HS Torr, N Siddharth, Awni Hannun, Nicolas Usunier, and Gabriel Synnaeve. 2022 · 2022
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Prompting gpt-3 to be reliable
Chenglei Si, Zhe Gan, Zhengyuan Yang, Shuohang Wang, Jianfeng Wang, Jordan Boyd-Graber, and Lijuan Wang. 2022 · 2022
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Supervising Model Attention with Human Explanations for Robust Natural Language Inference
Joe Stacey, Yonatan Belinkov, and Marek Rei. 2022 · 2022
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Gradient Methods Provably Converge to Non-Robust Networks
Gal Vardi, Gilad Yehudai, and Ohad Shamir. 2022 · 2022
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Do Prompt-Based Models Really Understand the Meaning of Their Prompts?. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Albert Webson and Ellie Pavlick. 2022 · 2022
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Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. In Advances in Neural Information Processing Systems
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed H Chi, Quoc V Le, Denny Zhou, et al · 2022
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TableFormer: Robust Transformer Modeling for Table-Text Encoding
Jingfeng Yang, Aditya Gupta, Shyam Upadhyay, Luheng He, Rahul Goel, and Shachi Paul. 2022 · 2022
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Robustness Challenges in Model Distillation and Pruning for Natural Language Understanding
Mengnan Du, Subhabrata Mukherjee, Yu Cheng, Milad Shokouhi, Xia Hu, and Ahmed Hassan Awadallah. 2023 · 2023
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Simplicity Bias in 1-Hidden Layer Neural Networks
Depen Morwani, Jatin Batra, Prateek Jain, and Praneeth Netrapalli. 2023 · 2023
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PECO: Examining Single Sentence Label Leakage in Natural Language Inference Datasets through Progressive Evaluation of Cluster Outliers. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics . 3053–3066
Michael Saxon, Xinyi Wang, Wenda Xu, and William Yang Wang. 2023 · 2023
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