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The alignment of large language models (LLMs) with human preferences remains a key challenge.
On information and sufficiency
Kullback, S. and Leibler, R. A · 1951
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Generating sequences with recurrent neural networks
Graves, A · 2013
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Adam: A method for stochastic optimization
Kingma, D. P · 2014
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Strong np-hardness for sparse optimization with concave penalty functions
Chen, Y., Ge, D., Wang, M., Wang, Z., Ye, Y., and Yin, H · 2017
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Deep reinforcement learning from human preferences
Christiano, P. F., Leike, J., Brown, T., Martic, M., Legg, S., and Amodei, D · 2017
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Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O · 2017
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Direct behavior specification via constrained reinforcement learning
Roy, J., Girgis, R., Romoff, J., Bacon, P.-L., and Pal, C · 2021
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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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Finetuned language models are zero-shot learners
Wei, J., Bosma, M., Zhao, V., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., and Le, Q. V · 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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Enhancing chat language models by scaling high-quality instructional conversations
Ding, N., Chen, Y., Xu, B., Qin, Y., Zheng, Z., Hu, S., Liu, Z., Sun, M., and Zhou, B · 2023
Cited alongside, same era.
Alpacaeval: An automatic evaluator of instruction-following models
Li, X., Zhang, T., Dubois, Y., Taori, R., Gulrajani, I., Guestrin, C., Liang, P., and Hashimoto, T. B · 2023
Cited alongside, same era.
Zephyr: Direct distillation of lm alignment
Tunstall, L., Beeching, E., Lambert, N., Rajani, N., Rasul, K., Belkada, Y., Huang, S., von Werra, L., Fourrier, C., Habib, N., et al · 2023
Cited alongside, same era.
Aligning large language models with human: A survey
Wang, Y., Zhong, W., Li, L., Mi, F., Zeng, X., Huang, W., Shang, L., Jiang, X., and Liu, Q · 2023
Cited alongside, same era.
Delve into ppo: Implementation matters for stable rlhf
Reference-free monolithic preference optimization with odds ratio
Hong, J., Lee, N., and Thorne, J · 2024
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Sparse autoencoders find highly interpretable features in language models
Huben, R., Cunningham, H., Smith, L. R., Ewart, A., and Sharkey, L · 2024
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From live data to high-quality benchmarks: The arena-hard pipeline, April 2024
Li, T., Chiang, W., Frick, E., Dunlap, L., Zhu, B., Gonzalez, J. E., and Stoica, I · 2024
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Gemma scope: Open sparse autoencoders everywhere all at once on gemma 2
Lieberum, T., Rajamanoharan, S., Conmy, A., Smith, L., Sonnerat, N., Varma, V., Kramár, J., Dragan, A., Shah, R., and Nanda, N · 2024
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Understanding reference policies in direct preference optimization, 2024
Liu, Y., Liu, P., and Cohan, A · 2024
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Zheng, R., Dou, S., Gao, S., Hua, Y., Shen, W., Wang, B., Liu, Y., Jin, S., Zhou, Y., Xiong, L., et al · 2023
Cited alongside, same era.
Chatbot arena: An open platform for evaluating llms by human preference, 2024
Chiang, W.-L., Zheng, L., Sheng, Y., Angelopoulos, A. N., Li, T., Li, D., Zhang, H., Zhu, B., Jordan, M., Gonzalez, J. E., and Stoica, I · 2024
Cited alongside, same era.
Scaling instruction-finetuned language models
Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, Y., Wang, X., Dehghani, M., Brahma, S., et al · 2024
Cited alongside, same era.
Ultrafeedback: Boosting language models with high-quality feedback, 2024
Cui, G., Yuan, L., Ding, N., Yao, G., Zhu, W., Ni, Y., Xie, G., Liu, Z., and Sun, M · 2024
Cited alongside, same era.
Length-controlled alpacaeval: A simple way to debias automatic evaluators
Dubois, Y., Galambosi, B., Liang, P., and Hashimoto, T. B · 2024
Cited alongside, same era.
Kto: Model alignment as prospect theoretic optimization
Ethayarajh, K., Xu, W., Muennighoff, N., Jurafsky, D., and Kiela, D · 2024
Cited alongside, same era.
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
Cited in the paper.
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Simpo: Simple preference optimization with a reference-free reward
Meng, Y., Xia, M., and Chen, D · 2024
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Direct preference optimization: Your language model is secretly a reward model, 2024
Rafailov, R., Sharma, A., Mitchell, E., Manning, C. D., Ermon, S., and Finn, C · 2024
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Gemma 2: Improving open language models at a practical size, 2024
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Token-level direct preference optimization
Zeng, Y., Liu, G., Ma, W., Yang, N., Zhang, H., and Wang, J · 2024
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