[16] T. Gao, A. Fisch, and D. Chen. Making pre-trained language models better few-shot learners. arXiv preprint arXiv:2012.15723, 2020.
[17] Google. Lamda: Towards safe, grounded, and high-quality dialog models for everything. Website, 2023. 2023, https://bard.google.com/.
[18] H. Guo, B. Tan, Z. Liu, E. Xing, and Z. Hu. Efficient (soft) q-learning for text generation with limited good data. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 6969-6991, 2022.
[19] S. Hoory, A. Feder, A. Tendler, S. Erell, A. Peled-Cohen, I. Laish, H. Nakhost, U. Stemmer, A. Benjamini, A. Hassidim, et al. Learning and evaluating a differentially private pre-trained language model. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 1178–1189, 2021.
[20] D. Ippolito, F. Tramèr, M. Nasr, C. Zhang, M. Jagielski, K. Lee, C. A. Choquette-Choo, and N. Carlini. Preventing verbatim memorization in language models gives a false sense of privacy. arXiv preprint arXiv:2210.17546, 2022.
[21] B. Jayaraman, L. Wang, K. Knipmeyer, Q. Gu, and D. Evans. Revisiting membership inference under realistic assumptions. Proceedings on Privacy Enhancing Technologies, 2021(2), 2021.
[22] Z. Jiang, F. F. Xu, J. Araki, and G. Neubig. How can we know what language models know? Transactions of the Association for Computational Linguistics, 8:423-438, 2020.
[23] E. Kharitonov, M. Baroni, and D. Hupkes. How bpe affects memorization in transformers. arXiv preprint arXiv:2110.02782, 2021.
[24] B. Lester, R. Al-Rfou, and N. Constant. The power of scale for parameter-efficient prompt tuning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Nov. 2021.
[25] X. Li, F. Tramer, P. Liang, and T. Hashimoto. Large language models can be strong differentially private learners. In International Conference on Learning Representations, 2022.
[26] X. L. Li and P. Liang. Prefix-tuning: Optimizing continuous prompts for generation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 4582-4597, 2021.
[27] J. Liu, D. Shen, Y. Zhang, B. Dolan, L. Carin, and W. Chen. What makes good in-context examples for gpt-3? arXiv preprint arXiv:2101.06804, 2021.
[28] X. Liu, K. Ji, Y. Fu, W. Tam, Z. Du, Z. Yang, and J. Tang. P-tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 61-68, Dublin, Ireland, May 2022. Association for Computational Linguistics.
[29] X. Liu, K. Ji, Y. Fu, W. L. Tam, Z. Du, Z. Yang, and J. Tang. P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks. arXiv preprint arXiv:2110.07602, 2021.
[30] Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov. Ro{bert}a: A robustly optimized {bert} pretraining approach, 2020.
[31] A. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts. Learning word vectors for sentiment analysis. In Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies, pages 142-150, 2011.
[32] R. T. McCoy, P. Smolensky, T. Linzen, J. Gao, and A. Celikyilmaz. How much do language models copy from their training data? evaluating linguistic novelty in text generation using raven. arXiv preprint arXiv:2111.09509, 2021.
[33] F. Mireshghallah, A. Uniyal, T. Wang, D. Evans, and T. Berg-Kirkpatrick. Memorization in nlp fine-tuning methods. arXiv preprint arXiv:2205.12506, 2022.