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AI developers often apply safety alignment procedures to prevent the misuse of their AI systems.
Hellaswag: Can a machine really finish your sentence?
Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., and Choi, Y. (2019) · 2019
Earlier work this paper cites.
Fine-tuning language models from human preferences
Ziegler, D. M., Stiennon, N., Wu, J., Brown, T. B., Radford, A., Amodei, D., Christiano, P., and Irving, G. (2020) · 2020
Earlier work this paper cites.
Measuring massive multitask language understanding
Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., and Steinhardt, J. (2021) · 2021
Earlier work this paper cites.
Lora: Low-rank adaptation of large language models
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W. (2021) · 2021
Earlier work this paper cites.
Constitutional ai: Harmlessness from ai feedback
Bai, Y., Kadavath, S., Kundu, S., Askell, A., Kernion, J., Jones, A., Chen, A., Goldie, A., Mirhoseini, A., McKinnon, C., Chen, C., Olsson, C., Olah, C., Hernandez, D., Drain, D., Ganguli, D., Li, D., Tran-Johnson, E., Perez, E., Kerr, J., Mueller, J., Ladish, J., Landau, J., Ndousse, K., Lukosuite, K., Lovitt, L., Sellitto, M., Elhage, N., Schiefer, N., Mercado, N., DasSarma, N., Lasenby, R., Larson, R., Ringer, S., Johnston, S., Kravec, S., Showk, S. E., Fort, S., Lanham, T., Telleen-Lawton, T., Conerly, T., Henighan, T., Hume, T., Bowman, S. R., Hatfield-Dodds, Z., Mann, B., Amodei, D., Joseph, N., McCandlish, S., Brown, T., and Kaplan, J. (2022) · 2022
Earlier work this paper cites.
Glam: Efficient scaling of language models with mixture-of-experts
Du, N., Huang, Y., Dai, A. M., Tong, S., Lepikhin, D., Xu, Y., Krikun, M., Zhou, Y., Yu, A. W., Firat, O., Zoph, B., Fedus, L., Bosma, M., Zhou, Z., Wang, T., Wang, Y. E., Webster, K., Pellat, M., Robinson, K., Meier-Hellstern, K., Duke, T., Dixon, L., Zhang, K., Le, Q. V., Wu, Y., Chen, Z., and Cui, C. (2022) · 2022
Earlier work this paper cites.
Red teaming language models with language models
Perez, E., Huang, S., Song, F., Cai, T., Ring, R., Aslanides, J., Glaese, A., McAleese, N., and Irving, G. (2022) · 2022
Earlier work this paper cites.
Non-transferable learning: A new approach for model ownership verification and applicability authorization
Wang, L., Xu, S., Xu, R., Wang, X., and Zhu, Q. (2022) · 2022
Earlier work this paper cites.
Qlora: Efficient finetuning of quantized llms
Dettmers, T., Pagnoni, A., Holtzman, A., and Zettlemoyer, L. (2023) · 2023
Earlier work this paper cites.
Will releasing the weights of large language models grant widespread access to pandemic agents?
Gopal, A., Helm-Burger, N., Justen, L., Soice, E. H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., and Esvelt, K. M. (2023) · 2023
Cited alongside, same era.
Language models represent space and time
Gurnee, W. and Tegmark, M. (2023) · 2023
Cited alongside, same era.
Self-destructing models: Increasing the costs of harmful dual uses of foundation models
Henderson, P., Mitchell, E., Manning, C. D., Jurafsky, D., and Finn, C. (2023) · 2023
Cited alongside, same era.
Evaluating language-model agents on realistic autonomous tasks
Kinniment, M., Koba Sato, L. J., Du, H., Goodrich, B., Hasin, M., Chan, L., Miles, L. H., Lin, T. R., Wijk, H., Burget, J., Ho, A., Barnes, E., and Christiano, P. (2023) · 2023
Cited alongside, same era.
How to catch an ai liar: Lie detection in black-box llms by asking unrelated questions
Pacchiardi, L., Chan, A. J., Mindermann, S., Moscovitz, I., Pan, A. Y., Gal, Y., Evans, O., and Brauner, J. (2023) · 2023
Cited alongside, same era.
Shadow alignment: The ease of subverting safely-aligned language models
Yang, X., Wang, X., Zhang, Q., Petzold, L., Wang, W. Y., Zhao, X., and Lin, D. (2023) · 2023
Closest in time.
Llm agents can autonomously hack websites
Fang, R., Bindu, R., Gupta, A., Zhan, Q., and Kang, D. (2024) · 2024
Closest in time.
Cybersecurity and ai: The evolving security landscape
Hendrycks, D. (2024) · 2024
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Mixtral of experts
Jiang, A. Q., Sablayrolles, A., Roux, A., Mensch, A., Savary, B., Bamford, C., Chaplot, D. S., de las Casas, D., Hanna, E. B., Bressand, F., Lengyel, G., Bour, G., Lample, G., Lavaud, L. R., Saulnier, L., Lachaux, M.-A., Stock, P., Subramanian, S., Yang, S., Antoniak, S., Scao, T. L., Gervet, T., Lavril, T., Wang, T., Lacroix, T., and Sayed, W. E. (2024) · 2024
Closest in time.
The Operational Risks of AI in Large-Scale Biological Attacks: Results of a Red-Team Study
Mouton, C. A., Lucas, C., and Guest, E. (2024) · 2024
Closest in time.
Building an early warning system for llm-aided biological threat creation
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Fine-tuning aligned language models compromises safety, even when users do not intend to!
Qi, X., Zeng, Y., Xie, T., Chen, P.-Y., Jia, R., Mittal, P., and Henderson, P. (2023) · 2023
Cited alongside, same era.
Direct preference optimization: Your language model is secretly a reward model
Rafailov, R., Sharma, A., Mitchell, E., Manning, C. D., Ermon, S., and Finn, C. (2023) · 2023
Cited alongside, same era.
Llama 2: Open foundation and fine-tuned chat models
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., Bikel, D., Blecher, L., Ferrer, C. C., Chen, M., Cucurull, G., Esiobu, D., Fernandes, J., Fu, J., Fu, W., Fuller, B., Gao, C., Goswami, V., Goyal, N., Hartshorn, A., Hosseini, S., Hou, R., Inan, H., Kardas, M., Kerkez, V., Khabsa, M., Kloumann, I., Korenev, A., Koura, P. S., Lachaux, M.-A., Lavril, T., Lee, J., Liskovich, D., Lu, Y., Mao, Y., Martinet, X., Mihaylov, T., Mishra, P., Molybog, I., Nie, Y., Poulton, A., Reizenstein, J., Rungta, R., Saladi, K., Schelten, A., Silva, R., Smith, E. M., Subramanian, R., Tan, X. E., Tang, B., Taylor, R., Williams, A., Kuan, J. X., Xu, P., Yan, Z., Zarov, I., Zhang, Y., Fan, A., Kambadur, M., Narang, S., Rodriguez, A., Stojnic, R., Edunov, S., and Scialom, T. (2023) · 2023
Cited alongside, same era.
Decodingtrust: A comprehensive assessment of trustworthiness in gpt models
Wang, B., Chen, W., Pei, H., Xie, C., Kang, M., Zhang, C., Xu, C., Xiong, Z., Dutta, R., Schaeffer, R., et al. (2023) · 2023
Cited alongside, same era.
Representation engineering: A top-down approach to ai transparency
Zou, A., Phan, L., Chen, S., Campbell, J., Guo, P., Ren, R., Pan, A., Yin, X., Mazeika, M., Dombrowski, A.-K., Goel, S., Li, N., Byun, M. J., Wang, Z., Mallen, A., Basart, S., Koyejo, S., Song, D., Fredrikson, M., Kolter, J. Z., and Hendrycks, D. (2023a)
Cited in the paper.
Universal and transferable adversarial attacks on aligned language models
Zou, A., Wang, Z., Kolter, J. Z., and Fredrikson, M. (2023b)
Cited in the paper.
OpenAI (2024) · 2024
Closest in time.
Mitigating fine-tuning jailbreak attack with backdoor enhanced alignment
Wang, J., Li, J., Li, Y., Qi, X., Hu, J., Li, Y., McDaniel, P., Chen, M., Li, B., and Xiao, C. (2024) · 2024
Closest in time.
Assessing the brittleness of safety alignment via pruning and low-rank modifications
Wei, B., Huang, K., Huang, Y., Xie, T., Qi, X., Xia, M., Mittal, P., Wang, M., and Henderson, P. (2024) · 2024
Closest in time.