[162] The Human Rights Campaign. Myths about hiv. https://www.hrc.org/resources/ debunking-common-myths-about-hiv, 2023.
[163] J. Thorne and A. Vlachos. Adversarial attacks against fact extraction and verification. CoRR, abs/1903.05543, 2019. URL http://arxiv.org/abs/1903.05543.
[164] H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozière, N. Goyal, E. Hambro, F. Azhar, et al. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023.
[165] H. Touvron, L. Martin, K. Stone, P. Albert, A. Almahairi, Y. Babaei, N. Bashlykov, S. Batra, P. Bhargava, S. Bhosale, D. Bikel, L. Blecher, C. Canton-Ferrer, M. Chen, G. Cucurull, D. Esiobu, J. Fernandes, J. Fu, W. Fu, B. Fuller, C. Gao, V. Goswami, N. Goyal, A. Hartshorn, S. Hosseini, R. Hou, H. Inan, M. Kardas, V. Kerkez, M. Khabsa, I. Kloumann, A. Korenev, P. S. Koura, M. Lachaux, T. Lavril, J. Lee, D. Liskovich, Y. Lu, Y. Mao, X. Martinet, T. Mihaylov, P. Mishra, I. Molybog, Y. Nie, A. Poulton, J. Reizenstein, R. Rungta, K. Saladi, A. Schelten, R. Silva, E. M. Smith, R. Subramanian, X. E. Tan, B. Tang, R. Taylor, A. Williams, J. X. Kuan, P. Xu, Z. Yan, I. Zarov, Y. Zhang, A. Fan, M. Kambadur, S. Narang, A. Rodriguez, R. Stojnic, S. Edunov, and T. Scialom. Llama 2: Open foundation and fine-tuned chat models. CoRR, abs/2307.09288, 2023. doi: 10.48550/arXiv.2307.09288. URL https://doi.org/10.48550/arXiv.2307.09288
[166] H. Touvron, L. Martin, K. Stone, P. Albert, A. Almahairi, Y. Babaei, N. Bashlykov, S. Batra, P. Bhargava, S. Bhosale, D. Bikel, L. Blecher, C. C. Ferrer, M. Chen, G. Cucurull, D. Esiobu, J. Fernandes, J. Fu, W. Fu, B. Fuller, C. Gao, V. Goswami, N. Goyal, A. Hartshorn, S. Hosseini, R. Hou, H. Inan, M. Kardas, V. Kerkez, M. Khabsa, I. Kloumann, A. Korenev, P. S. Koura, M.-A. Lachaux, T. Lavril, J. Lee, D. Liskovich, Y. Lu, Y. Mao, X. Martinet, T. Mihaylov, P. Mishra, I. Molybog, Y. Nie, A. Poulton, J. Reizenstein, R. Rungta, K. Saladi, A. Schelten, R. Silva, E. M. Smith, R. Subramanian, X. E. Tan, B. Tang, R. Taylor, A. Williams, J. X. Kuan, P. Xu, Z. Yan, I. Zarov, Y. Zhang, A. Fan, M. Kambadur, S. Narang, A. Rodriguez, R. Stojnic, S. Edunov, and T. Scialom. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv: 2307.09288, 2023.
[167] F. Tram'er, K. Gautam, and N. C. Carlini. Considerations for differentially private learning with large-scale public pretraining. arXiv:2212.06470, 2022.
[168] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin. Attention is all you need. In NIPS, 2017.
[169] S. D. Visco. Yellow peril, red scare: race and communism in national review. Ethnic and Racial Studies, 42(4):626-644, 2019. doi: 10.1080/01419870.2017.1409900. URL https://doi.org/10.1080/01419870.2017.1409900.
[170] E. Wallace, S. Feng, N. Kandpal, M. Gardner, and S. Singh. Universal adversarial triggers for attacking and analyzing nlp. In EMNLP, 2019.
[171] A. Wang, Y. Pruksachatkun, N. Nangia, A. Singh, J. Michael, F. Hill, O. Levy, and S. R. Bowman. Superglue: A stickier benchmark for general-purpose language understanding systems. In NeurIPS, 2019.
[172] A. Wang, A. Singh, J. Michael, F. Hill, O. Levy, and S. R. Bowman. Glue: A multi-task benchmark and analysis platform for natural language understanding. In ICLR, 2019.
[173] B. Wang, H. Pei, B. Pan, Q. Chen, S. Wang, and B. Li. T3: tree-autoencoder constrained adversarial text generation for targeted attack. In B. Webber, T. Cohn, Y. He, and Y. Liu, editors, Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020, pages 6134-6150. Association for Computational Linguistics, 2020. doi: 10.18653/v1/2020.emnlp-main.495. URL https://doi.org/10.18653/v1/2020.emnlp-main.495.
[174] B. Wang, S. Wang, Y. Cheng, Z. Gan, R. Jia, B. Li, and J. Liu. Infobert: Improving robustness of language models from an information theoretic perspective. In ICLR, 2021.
[175] B. Wang, C. Xu, S. Wang, Z. Gan, Y. Cheng, J. Gao, A. H. Awadallah, and B. Li. Adversarial GLUE: A multi-task benchmark for robustness evaluation of language models. In J. Vanschooren and S. Yeung, editors, Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, NeurIPS Datasets and Benchmarks 2021, December