Make Them Spill the Beans! Coercive Knowledge Extraction from (Production) LLMs

“Huggingface open llm leaderboard.” https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard , accessed: 2010-09-30

2010

Earlier work this paper cites.

“The yi series models are large language models trained from scratch by developers at 01.ai.” https://github.com/01-ai/Yi , accessed: 2010-09-30

2010

Earlier work this paper cites.

“trojan detection challenge 2023,” https://trojandetection.ai , accessed: 2010-09-30

2010

Earlier work this paper cites.

S. Griffith, K. Subramanian, J. Scholz, C. L. Isbell, and A. L. Thomaz, “Policy shaping: Integrating human feedback with reinforcement learning,” Advances in neural information processing systems , vol. 26, 2013

2013

Earlier work this paper cites.

A. P. Parikh, O. Täckström, D. Das, and J. Uszkoreit, “A decomposable attention model for natural language inference,” arXiv preprint arXiv:1606.01933 , 2016

Original

2016

Earlier work this paper cites.

P.-Y. Chen, H. Zhang, Y. Sharma, J. Yi, and C.-J. Hsieh, “Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models,” in Proceedings of the 10th ACM workshop on artificial intelligence and security , 2017, pp. 15–26

2017

Earlier work this paper cites.

J. Ebrahimi, A. Rao, D. Lowd, and D. Dou, “Hotflip: White-box adversarial examples for text classification,” arXiv preprint arXiv:1712.06751 , 2017

Original

2017

Earlier work this paper cites.

R. Paulus, C. Xiong, and R. Socher, “A deep reinforced model for abstractive summarization,” arXiv preprint arXiv:1705.04304 , 2017

Original

2017

Earlier work this paper cites.

B. Chen, W. Carvalho, N. Baracaldo, H. Ludwig, B. Edwards, T. Lee, I. Molloy, and B. Srivastava, “Detecting backdoor attacks on deep neural networks by activation clustering,” arXiv preprint arXiv:1811.03728 , 2018

Original

2018

Earlier work this paper cites.

B. Tran, J. Li, and A. Madry, “Spectral signatures in backdoor attacks,” Advances in neural information processing systems , vol. 31, 2018

2018

Earlier work this paper cites.

S. Cheng, Y. Dong, T. Pang, H. Su, and J. Zhu, “Improving black-box adversarial attacks with a transfer-based prior,” Advances in neural information processing systems , vol. 32, 2019

2019

Earlier work this paper cites.

A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , “Language models are unsupervised multitask learners,” OpenAI blog , vol. 1, no. 8, p. 9, 2019

2019

Earlier work this paper cites.

Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov, “Roberta: A robustly optimized bert pretraining approach,” arXiv preprint arXiv:1907.11692 , 2019

Original

2019

Earlier work this paper cites.

C. Guo, A. Sablayrolles, H. Jégou, and D. Kiela, “Gradient-based adversarial attacks against text transformers,” arXiv preprint arXiv:2104.13733 , 2021

Original

2021

Earlier work this paper cites.

J. Hayase, W. Kong, R. Somani, and S. Oh, “Spectre: Defending against backdoor attacks using robust statistics,” in International Conference on Machine Learning . PMLR, 2021, pp. 4129–4139

2021

Earlier work this paper cites.

L. Bourtoule, V. Chandrasekaran, C. A. Choquette-Choo, H. Jia, A. Travers, B. Zhang, D. Lie, and N. Papernot, “Machine unlearning,” in 2021 IEEE Symposium on Security and Privacy (SP) . IEEE, 2021, pp. 141–159

2021

Earlier work this paper cites.

A. Sekhari, J. Acharya, G. Kamath, and A. T. Suresh, “Remember what you want to forget: Algorithms for machine unlearning,” Advances in Neural Information Processing Systems , vol. 34, pp. 18 075–18 086, 2021

2021

Earlier work this paper cites.

F. Croce, M. Andriushchenko, N. D. Singh, N. Flammarion, and M. Hein, “Sparse-rs: a versatile framework for query-efficient sparse black-box adversarial attacks,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 36, no. 6, 2022, pp. 6437–6445

2022

Earlier work this paper cites.

J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V. Le, D. Zhou et al. , “Chain-of-thought prompting elicits reasoning in large language models,” Advances in Neural Information Processing Systems , vol. 35, pp. 24 824–24 837, 2022

2022

Earlier work this paper cites.

T. Kojima, S. S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa, “Large language models are zero-shot reasoners,” Advances in neural information processing systems , vol. 35, pp. 22 199–22 213, 2022

2022

Cited alongside, same era.

L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray et al. , “Training language models to follow instructions with human feedback,” Advances in Neural Information Processing Systems , vol. 35, pp. 27 730–27 744, 2022

2022

Cited alongside, same era.

A. Glaese, N. McAleese, M. Trebacz, J. Aslanides, V. Firoiu, T. Ewalds, M. Rauh, L. Weidinger, M. Chadwick, P. Thacker et al. , “Improving alignment of dialogue agents via targeted human judgements,” arXiv preprint arXiv:2209.14375 , 2022

Original

2022

Cited alongside, same era.

Y. Bai, A. Jones, K. Ndousse, A. Askell, A. Chen, N. DasSarma, D. Drain, S. Fort, D. Ganguli, T. Henighan et al. , “Training a helpful and harmless assistant with reinforcement learning from human feedback,” arXiv preprint arXiv:2204.05862 , 2022

Original

R. Lapid, R. Langberg, and M. Sipper, “Open sesame! universal black box jailbreaking of large language models,” arXiv preprint arXiv:2309.01446 , 2023

Original

2023

Closest in time.

B. Cao, Y. Cao, L. Lin, and J. Chen, “Defending against alignment-breaking attacks via robustly aligned llm,” arXiv preprint arXiv:2309.14348 , 2023

Original

2023

Closest in time.

P. Ding, J. Kuang, D. Ma, X. Cao, Y. Xian, J. Chen, and S. Huang, “A wolf in sheep’s clothing: Generalized nested jailbreak prompts can fool large language models easily,” arXiv preprint arXiv:2311.08268 , 2023

Original

2023

Closest in time.

J. Wang, Y. Liang, F. Meng, H. Shi, Z. Li, J. Xu, J. Qu, and J. Zhou, “Is chatgpt a good nlg evaluator? a preliminary study,” arXiv preprint arXiv:2303.04048 , 2023

Original

2023

Closest in time.