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As large language models (LLMs) become more capable, fine-tuning techniques for aligning with human intent are increasingly important.
Rank analysis of incomplete block designs: I. the method of paired comparisons
Bradley, R. A. and Terry, M. E · 1952
Earlier work this paper cites.
Deep bayesian active learning with image data
Gal, Y., Islam, R., and Ghahramani, Z · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O · 2017
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Earlier work this paper cites.
Online structured laplace approximations for overcoming catastrophic forgetting
Ritter, H., Botev, A., and Barber, D · 2018
Earlier work this paper cites.
Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning
Kirsch, A., Van Amersfoort, J., and Gal, Y · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al · 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 · 2019
Earlier work this paper cites.
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., et al · 2020
Earlier work this paper cites.
Learning to summarize with human feedback
Stiennon, N., Ouyang, L., Wu, J., Ziegler, D., Lowe, R., Voss, C., Radford, A., Amodei, D., and Christiano, P. F · 2020
Cited alongside, same era.
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
Cited alongside, same era.
A survey of deep active learning
Ren, P., Xiao, Y., Chang, X., Huang, P.-Y., Li, Z., Gupta, B. B., Chen, X., and Wang, X · 2021
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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., et al · 2022
Cited alongside, same era.
Language models (mostly) know what they know
Kadavath, S., Conerly, T., Askell, A., Henighan, T., Drain, D., Perez, E., Schiefer, N., Hatfield-Dodds, Z., DasSarma, N., Tran-Johnson, E., et al · 2022
Raft: Reward ranked finetuning for generative foundation model alignment
Dong, H., Xiong, W., Goyal, D., Pan, R., Diao, S., Zhang, J., Shum, K., and Zhang, T · 2023
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Benchmarking cognitive biases in large language models as evaluators
Koo, R., Lee, M., Raheja, V., Park, J. I., Kim, Z. M., and Kang, D · 2023
Later among the works it cites.
Active learning principles for in-context learning with large language models
Margatina, K., Schick, T., Aletras, N., and Dwivedi-Yu, J · 2023
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When less is more: Investigating data pruning for pretraining llms at scale
Marion, M., Üstün, A., Pozzobon, L., Wang, A., Fadaee, M., and Hooker, S · 2023
Later among the works it cites.
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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
Cited alongside, same era.
Pythia: A suite for analyzing large language models across training and scaling
Biderman, S., Schoelkopf, H., Anthony, Q. G., Bradley, H., O’Brien, K., Hallahan, E., Khan, M. A., Purohit, S., Prashanth, U. S., Raff, E., et al · 2023
Cited alongside, same era.
Chen, Y., Wang, R., Jiang, H., Shi, S., and Xu, R · 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., et al · 2023
Cited alongside, same era.
Rafailov, R., Sharma, A., Mitchell, E., Ermon, S., Manning, C. D., and Finn, C · 2023
Later among the works it cites.
Self-play fine-tuning converts weak language models to strong language models
Chen, Z., Deng, Y., Yuan, H., Ji, K., and Gu, Q · 2024
Closest in time.
Direct language model alignment from online ai feedback
Guo, S., Zhang, B., Liu, T., Liu, T., Khalman, M., Llinares, F., Rame, A., Mesnard, T., Zhao, Y., Piot, B., et al · 2024
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Self-rewarding language models
Yuan, W., Pang, R. Y., Cho, K., Sukhbaatar, S., Xu, J., and Weston, J · 2024
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