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In the post-training of large language models (LLMs), Reinforcement Learning from Human Feedback (RLHF) is an effective approach to achieve generation aligned with human preferences.
Rank analysis of incomplete block designs: I. the method of paired comparisons
Bradley, R. A. and Terry, M. E · 1952
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The pyramid method: Incorporating human content selection variation in summarization evaluation
Nenkova, A., Passonneau, R., and McKeown, K · 2007
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Novelty and diversity in information retrieval evaluation
Clarke, C. L., Kolla, M., Cormack, G. V., Vechtomova, O., Ashkan, A., Büttcher, S., and MacKinnon, I · 2008
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Non-saturating gan training as divergence minimization, 2020
Shannon, M., Poole, B., Mariooryad, S., Bagby, T., Battenberg, E., Kao, D., Stanton, D., and Skerry-Ryan, R · 2010
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How (not) to train your generative model: Scheduled sampling, likelihood, adversary?, 2015
Huszár, F · 2015
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A diversity-promoting objective function for neural conversation models
Li, J., Galley, M., Brockett, C., Gao, J., and Dolan, B · 2016
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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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Hierarchical neural story generation
Fan, A., Lewis, M., and Dauphin, Y · 2018
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Texygen: A benchmarking platform for text generation models
Zhu, Y., Lu, S., Zheng, L., Guo, J., Zhang, W., Wang, J., and Yu, Y · 2018
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Unifying human and statistical evaluation for natural language generation
Hashimoto, T. B., Zhang, H., and Liang, P · 2019
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Spoc: Search-based pseudocode to code
Kulal, S., Pasupat, P., Chandra, K., Lee, M., Padon, O., Aiken, A., and Liang, P. S · 2019
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Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al · 2019
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Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D · 2020
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Implementation matters in deep rl: A case study on ppo and trpo
Engstrom, L., Ilyas, A., Santurkar, S., Tsipras, D., Janoos, F., Rudolph, L., and Madry, A · 2020
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The curious case of neural text degeneration
Holtzman, A., Buys, J., Du, L., Forbes, M., and Choi, Y · 2020
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On the dangers of stochastic parrots: Can language models be too big?
Bender, E. M., Gebru, T., McMillan-Major, A., and Shmitchell, S · 2021
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Evaluating large language models trained on code
Chen, M., Tworek, J., Jun, H., Yuan, Q., de Oliveira Pinto, H. P., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., Ray, A., Puri, R., Krueger, G., Petrov, M., Khlaaf, H., Sastry, G., Mishkin, P., Chan, B., Gray, S., Ryder, N., Pavlov, M., Power, A., Kaiser, L., Bavarian, M., Winter, C., Tillet, P., Such, F. P., Cummings, D., Plappert, M., Chantzis, F., Barnes, E., Herbert-Voss, A., Guss, W. H., Nichol, A., Paino, A., Tezak, N., Tang, J., Babuschkin, I., Balaji, S., Jain, S., Saunders, W., Hesse, C., Carr, A. N., Leike, J., Achiam, J., Misra, V., Morikawa, E., Radford, A., Knight, M., Brundage, M., Murati, M., Mayer, K., Welinder, P., McGrew, B., Amodei, D., McCandlish, S., Sutskever, I., and Zaremba, W · 2021
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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., Hesse, C., and Schulman, J · 2021
Cited alongside, same era.
Imitation learning as f-divergence minimization
Ke, L., Choudhury, S., Barnes, M., Sun, W., Lee, G., and Srinivasa, S · 2021
Cited alongside, same era.
Trading off diversity and quality in natural language generation
Zhang, H., Duckworth, D., Ippolito, D., and Neelakantan, A · 2021
Cited alongside, same era.
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
Cited alongside, same era.
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
Model evaluation for extreme risks
Shevlane, T., Farquhar, S., Garfinkel, B., Phuong, M., Whittlestone, J., Leung, J., Kokotajlo, D., Marchal, N., Anderljung, M., Kolt, N., et al · 2023
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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., et al · 2023
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Zephyr: Direct distillation of lm alignment, 2023
Tunstall, L., Beeching, E., Lambert, N., Rajani, N., Rasul, K., Belkada, Y., Huang, S., von Werra, L., Fourrier, C., Habib, N., Sarrazin, N., Sanseviero, O., Rush, A. M., and Wolf, T · 2023
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Self-consistency improves chain of thought reasoning in language models
Wang, X., Wei, J., Schuurmans, D., Le, Q. V., Chi, E. H., Narang, S., Chowdhery, A., and Zhou, D · 2023
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Cited alongside, same era.
A systematic evaluation of large language models of code
Xu, F. F., Alon, U., Neubig, G., and Hellendoorn, V. J · 2022
Cited alongside, same era.
Ask me anything: A simple strategy for prompting language models
Arora, S., Narayan, A., Chen, M. F., Orr, L., Guha, N., Bhatia, K., Chami, I., and Re, C · 2023
Cited alongside, same era.
Palm: Scaling language modeling with pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., Schuh, P., Shi, K., Tsvyashchenko, S., Maynez, J., Rao, A., Barnes, P., Tay, Y., Shazeer, N., Prabhakaran, V., Reif, E., Du, N., Hutchinson, B., Pope, R., Bradbury, J., Austin, J., Isard, M., Gur-Ari, G., Yin, P., Duke, T., Levskaya, A., Ghemawat, S., Dev, S., Michalewski, H., Garcia, X., Misra, V., Robinson, K., Fedus, L., Zhou, D., Ippolito, D., Luan, D., Lim, H., Zoph, B., Spiridonov, A., Sepassi, R., Dohan, D., Agrawal, S., Omernick, M., Dai, A. M., Pillai, T. S., Pellat, M., Lewkowycz, A., Moreira, E., Child, R., Polozov, O., Lee, K., Zhou, Z., Wang, X., Saeta, B., Diaz, M., Firat, O., Catasta, M., Wei, J., Meier-Hellstern, K., Eck, D., Dean, J., Petrov, S., and Fiedel, N · 2023
Cited alongside, same era.
Ultrafeedback: Boosting language models with high-quality feedback, 2023
Cui, G., Yuan, L., Ding, N., Yao, G., Zhu, W., Ni, Y., Xie, G., Liu, Z., and Sun, M · 2023
Cited alongside, same era.
Enhancing chat language models by scaling high-quality instructional conversations, 2023
Ding, N., Chen, Y., Xu, B., Qin, Y., Zheng, Z., Hu, S., Liu, Z., Sun, M., and Zhou, B · 2023
Cited alongside, same era.
Survey of hallucination in natural language generation
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., and Fung, P · 2023
Cited alongside, same era.
Jiang, A. Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D. S., Casas, D. d. l., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., et al · 2023
Cited alongside, same era.
Zhou, J., Lu, T., Mishra, S., Brahma, S., Basu, S., Luan, Y., Zhou, D., and Hou, L · 2023
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Back to basics: Revisiting REINFORCE-style optimization for learning from human feedback in LLMs
Ahmadian, A., Cremer, C., Gallé, M., Fadaee, M., Kreutzer, J., Pietquin, O., Üstün, A., and Hooker, S · 2024
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Large language monkeys: Scaling inference compute with repeated sampling, 2024
Brown, B., Juravsky, J., Ehrlich, R., Clark, R., Le, Q. V., Ré, C., and Mirhoseini, A · 2024
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Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Yang, A., Fan, A., et al · 2024
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A framework for few-shot language model evaluation, 07 2024
Gao, L., Tow, J., Abbasi, B., Biderman, S., Black, S., DiPofi, A., Foster, C., Golding, L., Hsu, J., Le Noac’h, A., Li, H., McDonell, K., Muennighoff, N., Ociepa, C., Phang, J., Reynolds, L., Schoelkopf, H., Skowron, A., Sutawika, L., Tang, E., Thite, A., Wang, B., Wang, K., and Zou, A · 2024
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A general theoretical paradigm to understand learning from human preferences
Gheshlaghi Azar, M., Daniel Guo, Z., Piot, B., Munos, R., Rowland, M., Valko, M., and Calandriello, D · 2024
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V-STar: Training verifiers for self-taught reasoners
Hosseini, A., Yuan, X., Malkin, N., Courville, A., Sordoni, A., and Agarwal, R · 2024
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Mindstar: Enhancing math reasoning in pre-trained llms at inference time
Kang, J., Li, X. Z., Chen, X., Kazemi, A., and Chen, B · 2024
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Let’s verify step by step
Lightman, H., Kosaraju, V., Burda, Y., Edwards, H., Baker, B., Lee, T., Leike, J., Schulman, J., Sutskever, I., and Cobbe, K · 2024
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Statistical rejection sampling improves preference optimization
Liu, T., Zhao, Y., Joshi, R., Khalman, M., Saleh, M., Liu, P. J., and Liu, J · 2024
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Preference ranking optimization for human alignment
Song, F., Yu, B., Li, M., Yu, H., Huang, F., Li, Y., and Wang, H · 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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Hot or cold? adaptive temperature sampling for code generation with large language models
Zhu, Y., Li, J., Li, G., Zhao, Y., Jin, Z., and Mei, H · 2024
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