Targeted Attack on GPT-Neo for the SATML Language Model Data Extraction Challenge

N. Carlini, S. Chien, M. Nasr, S. Song, A. Terzis, and F. Tramer, “Membership inference attacks from first principles,” in 2022 IEEE Symposium on Security and Privacy (SP) . IEEE, 2022, pp. 1897–1914

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Earlier work this paper cites.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research , vol. 12, pp. 2825–2830, 2011

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L. Gao, S. Biderman, S. Black, L. Golding, T. Hoppe, C. Foster, J. Phang, H. He, A. Thite, N. Nabeshima, S. Presser, and C. Leahy, “The Pile: An 800gb dataset of diverse text for language modeling,” arXiv preprint arXiv:2101.00027 , 2020

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2020

Earlier work this paper cites.

M. Feurer, K. Eggensperger, S. Falkner, M. Lindauer, and F. Hutter, “Auto-sklearn 2.0: The next generation,” arXiv preprint arXiv:2007.04074 , vol. 24, 2020

Original

2020

Earlier work this paper cites.

N. Carlini, F. Tramer, E. Wallace, M. Jagielski, A. Herbert-Voss, K. Lee, A. Roberts, T. Brown, D. Song, U. Erlingsson et al. , “Extracting training data from large language models,” in 30th USENIX Security Symposium (USENIX Security 21) , 2021, pp. 2633–2650

2021

Cited alongside, same era.

S. Black, L. Gao, P. Wang, C. Leahy, and S. Biderman, “GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow,” Mar. 2021, If you use this software, please cite it using these metadata. [Online]. Available: https://doi.org/10.5281/zenodo.5297715

2021

Cited alongside, same era.

M. Izadi, R. Gismondi, and G. Gousios, “Codefill: Multi-token code completion by jointly learning from structure and naming sequences,” in Proceedings of the 44th International Conference on Software Engineering (ICSE) . ACM, 2022, p. 401–412

2022

Cited alongside, same era.

H. Hu, Z. Salcic, L. Sun, G. Dobbie, P. S. Yu, and X. Zhang, “Membership inference attacks on machine learning: A survey,” ACM Computing Surveys (CSUR) , vol. 54, no. 11s, pp. 1–37, 2022

2022

Later among the works it cites.

F. Mireshghallah, K. Goyal, A. Uniyal, T. Berg-Kirkpatrick, and R. Shokri, “Quantifying privacy risks of masked language models using membership inference attacks,” arXiv preprint arXiv:2203.03929 , 2022

Original

2022

Later among the works it cites.

A. Al-Kaswan, T. Ahmed, M. Izadi, A. A. Sawant, P. Devanbu, and A. van Deursen, “Extending source code pre-trained language models to summarise decompiled binaries,” in Proceedings of the 30th IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) , 2023

2023

Closest in time.