Prompt-Based and Two-Stage Training for Few-Shot Text Classification
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-9637-7_2
Reference28 articles.
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3. Brown, T., et al.: Language models are few-shot learners. In: Advances in Neural Information Processing Systems, pp. 1877–1901. Curran Associates Inc (2020)
4. Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., Neubig, G.: Pre-train, prompt, and predict: a systematic survey of prompting methods in natural language processing. ACM Comput. Surv. 55(9) (2023). https://doi.org/10.1145/3560815
5. Schick, T., Schutze, H.: Exploiting cloze-questions for few-shot text classification and natural language inference. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp. 255–269. Association for Computational Linguistics (2021). https://doi.org/10.18653/v1/2021.eacl-main.20
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