Merging Improves Self-Critique Against Jailbreak Attacks

Simple and scalable predictive uncertainty estimation using deep ensembles

Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017

Earlier work this paper cites.

Towards deep learning models resistant to adversarial attacks

Original

Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2017

Earlier work this paper cites.

Loss surfaces, mode connectivity, and fast ensembling of dnns

Garipov, T., Izmailov, P., Podoprikhin, D., Vetrov, D. P., and Wilson, A. G · 2018

Earlier work this paper cites.

Averaging weights leads to wider optima and better generalization

Izmailov, P., Wilson, A., Podoprikhin, D., Vetrov, D., and Garipov, T · 2018

Earlier work this paper cites.

Lora: Low-rank adaptation of large language models

Original

Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W · 2021

Earlier work this paper cites.

Constitutional ai: Harmlessness from ai feedback

Original

Bai, Y., Kadavath, S., Kundu, S., Askell, A., Kernion, J., Jones, A., Chen, A., Goldie, A., Mirhoseini, A., McKinnon, C., et al · 2022

Earlier work this paper cites.

Training language models to follow instructions with human feedback, 2022

Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P., Leike, J., and Lowe, R · 2022

Earlier work this paper cites.

Chain-of-thought prompting elicits reasoning in large language models

Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., Zhou, D., et al · 2022

Earlier work this paper cites.

Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Wortsman, M., Ilharco, G., Gadre, S. Y., Roelofs, R., Gontijo-Lopes, R., Morcos, A. S., Namkoong, H., Farhadi, A., Carmon, Y., Kornblith, S., et al · 2022

Earlier work this paper cites.

Open llm leaderboard

Beeching, E., Fourrier, C., Habib, N., Han, S., Lambert, N., Rajani, N., Sanseviero, O., Tunstall, L., and Wolf, T · 2023

Earlier work this paper cites.

It’s mbr all the way down: Modern generation techniques through the lens of minimum bayes risk

Bertsch, A., Xie, A., Neubig, G., and Gormley, M. R · 2023

Earlier work this paper cites.

Are aligned neural networks adversarially aligned?

Original

Carlini, N., Nasr, M., Choquette-Choo, C. A., Jagielski, M., Gao, I., Awadalla, A., Koh, P. W., Ippolito, D., Lee, K., Tramer, F., et al · 2023

Earlier work this paper cites.

Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, March 2023

Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J. E., Stoica, I., and Xing, E. P · 2023

Earlier work this paper cites.

Rephrase and respond: Let large language models ask better questions for themselves

Original

Deng, Y., Zhang, W., Chen, Z., and Gu, Q · 2023

Cited alongside, same era.

Toxicity in chatgpt: Analyzing persona-assigned language models

Deshpande, A., Murahari, V., Rajpurohit, T., Kalyan, A., and Narasimhan, K · 2023

Cited alongside, same era.

Mistral 7b, 2023

Jiang, A. Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D. S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L. R., Lachaux, M.-A., Stock, P., Scao, T. L., Lavril, T., Wang, T., Lacroix, T., and Sayed, W. E · 2023

Cited alongside, same era.

Automatically auditing large language models via discrete optimization

Original

Jones, E., Dragan, A., Raghunathan, A., and Steinhardt, J · 2023

Cited alongside, same era.

Exploiting asymmetry for synthetic training data generation: SynthIE and the case of information extraction

Josifoski, M., Sakota, M., Peyrard, M., and West, R · 2023

Stanford alpaca: An instruction-following llama model

Taori, R., Gulrajani, I., Zhang, T., Dubois, Y., Li, X., Guestrin, C., Liang, P., and Hashimoto, T. B · 2023

Later among the works it cites.

Jailbroken: How does llm safety training fail?

Original

Wei, A., Haghtalab, N., and Steinhardt, J · 2023

Later among the works it cites.

TIES-merging: Resolving interference when merging models

Yadav, P., Tam, D., Choshen, L., Raffel, C., and Bansal, M · 2023

Later among the works it cites.

Autodan: Automatic and interpretable adversarial attacks on large language models

Original

Zhu, S., Zhang, R., An, B., Wu, G., Barrow, J., Wang, Z., Huang, F., Nenkova, A., and Sun, T · 2023

Later among the works it cites.

Universal and transferable adversarial attacks on aligned language models, 2023

Zou, A., Wang, Z., Kolter, J. Z., and Fredrikson, M · 2023

Later among the works it cites.