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Shortcut learning refers to the phenomenon where models employ simple, non-robust decision rules in practical tasks, which hinders their generalization and robustness.
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Moon, S.J., Mo, S., Lee, K., Lee, J., Shin, J.: MASKER: masked keyword regularization for reliable text classification. In: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021. pp. 13578–13586. AAAI Press (2021), https://doi.org/10.1609/aaai.v35i15.17601
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Wang, T., Sridhar, R., Yang, D., Wang, X.: Identifying and mitigating spurious correlations for improving robustness in NLP models. In: Findings of the Association for Computational Linguistics: NAACL 2022, Seattle, WA, United States, July 10-15, 2022. pp. 1719–1729. Association for Computational Linguistics (2022), https://doi.org/10.18653/v1/2022.findings-naacl.130
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Lu, Y., Bartolo, M., Moore, A., Riedel, S., Stenetorp, P.: Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2022, Dublin, Ireland, May 22-27, 2022. pp. 8086–8098. Association for Computational Linguistics (2022), https://doi.org/10.18653/v1/2022.acl-long.556
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Yang, N., Kang, T., Choi, S.J., Lee, H., Jung, K.: Mitigating biases for instruction-following language models via bias neurons elimination. In: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2024, Bangkok, Thailand, August 11-16, 2024. pp. 9061–9073. Association for Computational Linguistics (2024), https://doi.org/10.18653/v1/2024.acl-long.490
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Zhou, H., Wan, X., Proleev, L., Mincu, D., Chen, J., Heller, K.A., Roy, S.: Batch calibration: Rethinking calibration for in-context learning and prompt engineering. In: The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024. OpenReview.net (2024), https://openreview.net/forum?id=L3FHMoKZcS
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Jang, J., Jang, S., Kweon, W., Jeon, M., Yu, H.: Rectifying demonstration shortcut in in-context learning. In: Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), NAACL 2024, Mexico City, Mexico, June 16-21, 2024. pp. 4294–4321. Association for Computational Linguistics (2024), https://doi.org/10.18653/v1/2024.naacl-long.242
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Li, Z., Jiang, G., Xie, H., Song, L., Lian, D., Wei, Y.: Understanding and patching compositional reasoning in llms. In: Findings of the Association for Computational Linguistics, ACL 2024, Bangkok, Thailand and virtual meeting, August 11-16, 2024. pp. 9668–9688. Association for Computational Linguistics (2024), https://doi.org/10.18653/v1/2024.findings-acl.576
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Yoran, O., Wolfson, T., Ram, O., Berant, J.: Making retrieval-augmented language models robust to irrelevant context. In: The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024. OpenReview.net (2024), https://openreview.net/forum?id=ZS4m74kZpH
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Peng, K., Ding, L., Yuan, Y., Liu, X., Zhang, M., Ouyang, Y., Tao, D.: Revisiting demonstration selection strategies in in-context learning. In: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2024, Bangkok, Thailand, August 11-16, 2024. pp. 9090–9101. Association for Computational Linguistics (2024), https://doi.org/10.18653/v1/2024.acl-long.492
2024
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Siska, C., Marazopoulou, K., Ailem, M., Bono, J.: Examining the robustness of LLM evaluation to the distributional assumptions of benchmarks. In: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2024, Bangkok, Thailand, August 11-16, 2024. pp. 10406–10421. Association for Computational Linguistics (2024), https://doi.org/10.18653/v1/2024.acl-long.560
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Patnaik, S., Changwal, H., Aggarwal, M., Bhatia, S., Kumar, Y., Krishnamurthy, B.: CABINET: content relevance-based noise reduction for table question answering. In: The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024 (2024), https://openreview.net/forum?id=SQrHpTllXa
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Hu, Y., Tang, X., Yang, H., Zhang, M.: Case-based or rule-based: How do transformers do the math? In: Forty-first International Conference on Machine Learning, ICML 2024, Vienna, Austria, July 21-27, 2024. OpenReview.net (2024), https://openreview.net/forum?id=4Vqr8SRfyX
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