[AHS22] Samuel K Ainsworth, Jonathan Hayase, and Siddhartha Srinivasa. Git re-basin: Merging models modulo permutation symmetries. arXiv preprint arXiv:2209.04836, 2022.
[AL07] Yonatan Aumann and Yehuda Lindell. Security against covert adversaries: Efficient protocols for realistic adversaries. In Theory of Cryptography: 4th Theory of Cryptography Conference, TCC 2007, Amsterdam, The Netherlands, February 21-24, 2007. Proceedings 4, pages 137-156. Springer, 2007.
[BISZ+22] Mikel Bober-Irizar, Ilia Shumailov, Yiren Zhao, Robert Mullins, and Nicolas Papernot. Architectural backdoors in neural networks. arXiv preprint arXiv:2206.07840, 2022.
[BPS+23] Stella Biderman, USVSN Sai Prashanth, Lintang Sutawika, Hailey Schoelkopf, Quentin Anthony, Shivanshu Purohit, and Edward Raf. Emergent and predictable memorization in large language models. arXiv preprint arXiv:2304.11158, 2023.
[BSA+23] Stella Biderman, Hailey Schoelkopf, Quentin Anthony, Herbie Bradley, Kyle O'Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, Aviya Skowron, Lintang Sutawika, and Oskar van der Wal. Pythia: A suite for analyzing large language models across training and scaling, 2023.
[Cen23] Alignment Research Center. Update on ARC's recent eval efforts, March 2023.
[CKV10] Kai-Min Chung, Yael Kalai, and Salil Vadhan. Improved delegation of computation using fully homomorphic encryption. In Advances in Cryptology-CRYPTO 2010: 30th Annual Cryptology Conference, Santa Barbara, CA, USA, August 15-19, 2010. Proceedings 30, pages 483-501. Springer, 2010.
[FDRC20] Jonathan Frankle, Gintare Karolina Dziugaite, Daniel Roy, and Michael Carbin. Linear mode connectivity and the lottery ticket hypothesis. In International Conference on Machine Learning, pages 3259-3269. PMLR, 2020.
[FJT+22] Congyu Fang, Hengrui Jia, Anvith Thudi, Mohammad Yaghini, Christopher A Choquette-Choo, Natalie Dullerud, Varun Chandrasekaran, and Nicolas Papernot. On the fundamental limits of formally (dis) proving robustness in proof-of-learning. arXiv preprint arXiv:2208.03567, 2022.
[FZ20] Vitaly Feldman and Chiyuan Zhang. What neural networks memorize and why: Discovering the long tail via influence estimation. In H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems, volume 33, pages 2881-2891. Curran Associates, Inc., 2020.
$\left[\mathrm{GBB}^{+}20\right]$  Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, Shawn Presser, and Connor Leahy. The Pile: An 800gb dataset of diverse text for language modeling. arXiv preprint arXiv:2101.00027, 2020.
[GCPT19] Aaron Gokaslan, Vanya Cohen, Ellie Pavlick, and Stefanie Tellex. Openwebtext corpus, 2019.
[GKVZ22] Shafi Goldwasser, Michael P Kim, Vinod Vaikuntanathan, and Or Zamir. Planting undetectable backdoors in machine learning models. In 2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS), pages 931-942. IEEE, 2022.
$\left[\mathrm{GPAM}^{+}20\right]$  Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial networks. Communications of the ACM, 63(11):139–144, 2020.
[GTB22] Wei Guo, Benedetta Tondi, and Mauro Barni. An overview of backdoor attacks against deep neural networks and possible defences. IEEE Open Journal of Signal Processing, 2022.