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Ensuring the safety and harmlessness of Large Language Models (LLMs) has become equally critical as their performance in applications.
In two minds: dual-process accounts of reasoning
Evans, J. S. B · 2003
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An adaptive sampling algorithm for solving markov decision processes
Chang, H. S., Fu, M. C., Hu, J., and Marcus, S. I · 2005
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On monte carlo tree search and reinforcement learning
Vodopivec, T., Samothrakis, S., and Ster, B · 2017
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Evaluating large language models trained on code
Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. D. O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al · 2021
Earlier work this paper cites.
Training verifiers to solve math word problems
Cobbe, K., Kosaraju, V., Bavarian, M., Chen, M., Jun, H., Kaiser, L., Plappert, M., Tworek, J., Hilton, J., Nakano, R., et al · 2021
Earlier work this paper cites.
Measuring mathematical problem solving with the math dataset
Hendrycks, D., Burns, C., Kadavath, S., Arora, A., Basart, S., Tang, E., Song, D., and Steinhardt, J · 2021
Earlier work this paper cites.
Adversarial glue: A multi-task benchmark for robustness evaluation of language models
Wang, B., Xu, C., Wang, S., Gan, Z., Cheng, Y., Gao, J., Awadallah, A. H., and Li, B · 2021
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Training a helpful and harmless assistant with reinforcement learning from human feedback
Bai, Y., Jones, A., Ndousse, K., Askell, A., Chen, A., DasSarma, N., Drain, D., Fort, S., Ganguli, D., Henighan, T., et al · 2022
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Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned
Ganguli, D., Lovitt, L., Kernion, J., Askell, A., Bai, Y., Kadavath, S., Mann, B., Perez, E., Schiefer, N., Ndousse, K., et al · 2022
Earlier work this paper cites.
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al · 2022
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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
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Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al · 2023
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Bai, J., Bai, S., Chu, Y., Cui, Z., Dang, K., Deng, X., Fan, Y., Ge, W., Han, Y., Huang, F., et al · 2023
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How robust is google’s bard to adversarial image attacks?
Dong, Y., Chen, H., Chen, J., Fang, Z., Yang, X., Zhang, Y., Tian, Y., Su, H., and Zhu, J · 2023
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Reinforced self-training (rest) for language modeling
Gulcehre, C., Paine, T. L., Srinivasan, S., Konyushkova, K., Weerts, L., Sharma, A., Siddhant, A., Ahern, A., Wang, M., Gu, C., et al · 2023
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Large language models can self-improve
Huang, J., Gu, S., Hou, L., Wu, Y., Wang, X., Yu, H., and Han, J · 2023
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Jailbreaking chatgpt via prompt engineering: An empirical study
Liu, Y., Deng, G., Xu, Z., Li, Y., Zheng, Y., Zhang, Y., Zhao, L., Zhang, T., Wang, K., and Liu, Y · 2023
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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
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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
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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., Truong, S., Arora, S., Mazeika, M., Hendrycks, D., Lin, Z., Cheng, Y., Koyejo, S., Song, D., and Li, B · 2023
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Self-evaluation guided beam search for reasoning
Xie, Y., Kawaguchi, K., Zhao, Y., Zhao, J. X., Kan, M.-Y., He, J., and Xie, M · 2023
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Tree of thoughts: deliberate problem solving with large language models
Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T. L., Cao, Y., and Narasimhan, K · 2023
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Judging llm-as-a-judge with mt-bench and chatbot arena
Zheng, L., Chiang, W.-L., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., Lin, Z., Li, Z., Li, D., Xing, E., et al · 2023
Earlier work this paper cites.
Universal and transferable adversarial attacks on aligned language models
Zou, A., Wang, Z., Carlini, N., Nasr, M., Kolter, J. Z., and Fredrikson, M · 2023
Cited alongside, same era.
Foundational challenges in assuring alignment and safety of large language models
Anwar, U., Saparov, A., Rando, J., Paleka, D., Turpin, M., Hase, P., Lubana, E. S., Jenner, E., Casper, S., Sourbut, O., et al · 2024
Cited alongside, same era.
Step-level value preference optimization for mathematical reasoning
Chen, G., Liao, M., Li, C., and Fan, K · 2024
Cited alongside, same era.
Ultrafeedback: Boosting language models with scaled ai feedback
Cui, G., Yuan, L., Ding, N., Yao, G., He, B., Zhu, W., Ni, Y., Xie, G., Xie, R., Lin, Y., et al · 2024
Cited alongside, same era.
Safe rlhf: Safe reinforcement learning from human feedback
Dai, J., Pan, X., Sun, R., Ji, J., Xu, X., Liu, M., Wang, Y., and Yang, Y · 2024
Cited alongside, same era.
Scaling llm test-time compute optimally can be more effective than scaling model parameters
Snell, C., Lee, J., Xu, K., and Kumar, A · 2024
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A strongreject for empty jailbreaks
Souly, A., Lu, Q., Bowen, D., Trinh, T., Hsieh, E., Pandey, S., Abbeel, P., Svegliato, J., Emmons, S., Watkins, O., and Toyer, S · 2024
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Qwq: Reflect deeply on the boundaries of the unknown, November 2024
Team, Q · 2024
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Challenges and barriers of using large language models (llm) such as chatgpt for diagnostic medicine with a focus on digital pathology–a recent scoping review
Ullah, E., Parwani, A., Baig, M. M., and Singh, R · 2024
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Stepwise alignment for constrained language model policy optimization
Wachi, A., Tran, T. Q., Sato, R., Tanabe, T., and Akimoto, Y · 2024
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Attacks, defenses and evaluations for LLM conversation safety: A survey
Dong, Z., Zhou, Z., Yang, C., Shao, J., and Qiao, Y · 2024
Cited alongside, same era.
Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Yang, A., Fan, A., et al · 2024
Cited alongside, same era.
Length-controlled alpacaeval: A simple debiasing of automatic evaluators
Dubois, Y., Liang, P., and Hashimoto, T · 2024
Cited alongside, same era.
Deliberative alignment: Reasoning enables safer language models
Guan, M. Y., Joglekar, M., Wallace, E., Jain, S., Barak, B., Heylar, A., Dias, R., Vallone, A., Ren, H., Wei, J., et al · 2024
Cited alongside, same era.
Controllable preference optimization: Toward controllable multi-objective alignment
Guo, Y., Cui, G., Yuan, L., Ding, N., Sun, Z., Sun, B., Chen, H., Xie, R., Zhou, J., Lin, Y., Liu, Z., and Sun, M · 2024
Cited alongside, same era.
Openrlhf: An easy-to-use, scalable and high-performance rlhf framework
Hu, J., Wu, X., Zhu, Z., Xianyu, Wang, W., Zhang, D., and Cao, Y · 2024
Cited alongside, same era.
Jaech, A., Kalai, A., Lerer, A., Richardson, A., El-Kishky, A., Low, A., Helyar, A., Madry, A., Beutel, A., Carney, A., et al · 2024
Cited alongside, same era.
Alphazero-like tree-search can guide large language model decoding and training
Wan, Z., Feng, X., Wen, M., McAleer, S. M., Wen, Y., Zhang, W., and Wang, J · 2024
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Do-not-answer: Evaluating safeguards in LLMs
Wang, Y., Li, H., Han, X., Nakov, P., and Baldwin, T · 2024
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SELF-GUARD: Empower the LLM to safeguard itself
Wang, Z., Yang, F., Wang, L., Zhao, P., Wang, H., Chen, L., Lin, Q., and Wong, K.-F · 2024
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Thinking llms: General instruction following with thought generation
Wu, T., Lan, J., Yuan, W., Jiao, J., Weston, J., and Sukhbaatar, S · 2024
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Yang, A., Yang, B., Hui, B., Zheng, B., Yu, B., Zhou, C., Li, C., Li, C., Liu, D., Huang, F., Dong, G., Wei, H., Lin, H., Tang, J., Wang, J., Yang, J., Tu, J., Zhang, J., Ma, J., Xu, J., Zhou, J., Bai, J., He, J., Lin, J., Dang, K., Lu, K., Chen, K., Yang, K., Li, M., Xue, M., Ni, N., Zhang, P., Wang, P., Peng, R., Men, R., Gao, R., Lin, R., Wang, S., Bai, S., Tan, S., Zhu, T., Li, T., Liu, T., Ge, W., Deng, X., Zhou, X., Ren, X., Zhang, X., Wei, X., Ren, X., Fan, Y., Yao, Y., Zhang, Y., Wan, Y., Chu, Y., Liu, Y., Cui, Z., Zhang, Z., and Fan, Z · 2024
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Self-rewarding language models
Yuan, W., Pang, R. Y., Cho, K., Li, X., Sukhbaatar, S., Xu, J., and Weston, J. E · 2024
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How johnny can persuade LLMs to jailbreak them: Rethinking persuasion to challenge AI safety by humanizing LLMs
Zeng, Y., Lin, H., Zhang, J., Yang, D., Jia, R., and Shi, W · 2024
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Wildchat: 1m chatgpt interaction logs in the wild
Zhao, W., Ren, X., Hessel, J., Cardie, C., Choi, Y., and Deng, Y · 2024
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Llamafactory: Unified efficient fine-tuning of 100+ language models
Zheng, Y., Zhang, R., Zhang, J., Ye, Y., Luo, Z., Feng, Z., and Ma, Y · 2024
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Beyond one-preference-fits-all alignment: Multi-objective direct preference optimization
Zhou, Z., Liu, J., Shao, J., Yue, X., Yang, C., Ouyang, W., and Qiao, Y · 2024
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Improving alignment and robustness with circuit breakers
Zou, A., Phan, L., Wang, J., Duenas, D., Lin, M., Andriushchenko, M., Wang, R., Kolter, Z., Fredrikson, M., and Hendrycks, D · 2024
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Jailbreaking black box large language models in twenty queries
Chao, P., Robey, A., Dobriban, E., Hassani, H., Pappas, G. J., and Wong, E · 2025
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Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
Guo, D., Yang, D., Zhang, H., Song, J., Zhang, R., Xu, R., Zhu, Q., Ma, S., Wang, P., Bi, X., et al · 2025
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Breaking the ceiling: Exploring the potential of jailbreak attacks through expanding strategy space
Huang, Y., Sun, Y., Ruan, S., Zhang, Y., Dong, Y., and Wei, X · 2025
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Rethinking machine unlearning for large language models
Liu, S., Yao, Y., Jia, J., Casper, S., Baracaldo, N., Hase, P., Yao, Y., Liu, C. Y., Xu, X., Li, H., et al · 2025
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Safety alignment should be made more than just a few tokens deep
Qi, X., Panda, A., Lyu, K., Ma, X., Roy, S., Beirami, A., Mittal, P., and Henderson, P · 2025
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Self-memory alignment: Mitigating factual hallucinations with generalized improvement
Zhang, S., Zhang, Y., Dong, Y., and Su, H · 2025
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