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With the increasing capabilities of large language models (LLMs), these high-performance models have achieved state-of-the-art results on a wide range of natural language processing (NLP) tasks.
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Ruan, W., Nechaev, Y., Chen, L., Su, C., Kiss, I.: Towards an asr error robust spoken language understanding system. In: Interspeech 2020 (2020), https://www.amazon.science/publications/towards-an-asr-error-robust-spoken-language-understanding-system
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Gui, T., Wang, X., Zhang, Q., Liu, Q., Zou, Y., Zhou, X., Zheng, R., Zhang, C., Wu, Q., Ye, J., Pang, Z., Zhang, Y., Li, Z., Ma, R., Fei, Z., Cai, R., Zhao, J., Hu, X., Yan, Z., Tan, Y., Hu, Y., Bian, Q., Liu, Z., Zhu, B., Qin, S., Xing, X., Fu, J., Zhang, Y., Peng, M., Zheng, X., Zhou, Y., Wei, Z., Qiu, X., Huang, X.: Textflint: Unified multilingual robustness evaluation toolkit for natural language processing (2021)
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Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models (2021)
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Lin, J., Men, R., Yang, A., Zhou, C., Zhang, Y., Wang, P., Zhou, J., Tang, J., Yang, H.: M6: Multi-modality-to-multi-modality multitask mega-transformer for unified pretraining. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery Data Mining. p. 3251–3261. KDD ’21, Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3447548.3467206, https://doi.org/10.1145/3447548.3467206
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Moradi, M., Samwald, M.: Evaluating the robustness of neural language models to input perturbations. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. pp. 1558–1570. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic (Nov 2021). https://doi.org/10.18653/v1/2021.emnlp-main.117, https://aclanthology.org/2021.emnlp-main.117
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Dong, G., Guo, D., Wang, L., Li, X., Wang, Z., Zeng, C., He, K., Zhao, J., Lei, H., Cui, X., Huang, Y., Feng, J., Xu, W.: PSSAT: A perturbed semantic structure awareness transferring method for perturbation-robust slot filling. In: Proceedings of the 29th International Conference on Computational Linguistics. pp. 5327–5334. International Committee on Computational Linguistics, Gyeongju, Republic of Korea (Oct 2022), https://aclanthology.org/2022.coling-1.473
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Li, X., Wang, L., Dong, G., He, K., Zhao, J., Lei, H., Liu, J., Xu, W.: Generative zero-shot prompt learning for cross-domain slot filling with inverse prompting. In: Findings of the Association for Computational Linguistics: ACL 2023. pp. 825–834. Association for Computational Linguistics, Toronto, Canada (Jul 2023), https://aclanthology.org/2023.findings-acl.52
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Liu, J., Wang, L., Dong, G., Song, X., Wang, Z., Wang, Z., Lei, S., Zhao, J., He, K., Xiao, B., Xu, W.: Towards robust and generalizable training: An empirical study of noisy slot filling for input perturbations (2023)
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Li, X., Lei, H., Wang, L., Dong, G., Zhao, J., Liu, J., Xu, W., Zhang, C.: A robust contrastive alignment method for multi-domain text classification. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 7827–7831 (2022). https://doi.org/10.1109/ICASSP43922.2022.9747192
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Qixiang, G., Dong, G., Mou, Y., Wang, L., Zeng, C., Guo, D., Sun, M., Xu, W.: Exploiting domain-slot related keywords description for few-shot cross-domain dialogue state tracking. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. pp. 2460–2465. Association for Computational Linguistics, Abu Dhabi, United Arab Emirates (Dec 2022), https://aclanthology.org/2022.emnlp-main.157
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Zeng, W., He, K., Wang, Z., Fu, D., Dong, G., Geng, R., Wang, P., Wang, J., Sun, C., Wu, W., Xu, W.: Semi-supervised knowledge-grounded pre-training for task-oriented dialog systems. In: Proceedings of the Towards Semi-Supervised and Reinforced Task-Oriented Dialog Systems (SereTOD). pp. 39–47. Association for Computational Linguistics, Abu Dhabi, Beijing (Hybrid) (Dec 2022), https://aclanthology.org/2022.seretod-1.6
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Zhang, Y., Wang, S., Li, P., Dong, G., Wang, S., Xian, Y., Li, Z., Zhang, H.: Pay attention to implicit attribute values: A multi-modal generative framework for AVE task. In: Findings of the Association for Computational Linguistics: ACL 2023. pp. 13139–13151. Association for Computational Linguistics, Toronto, Canada (Jul 2023), https://aclanthology.org/2023.findings-acl.831
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