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Self-alignment is an effective way to reduce the cost of human annotation while ensuring promising model capability.
An intuitive proof of the data processing inequality
Beaudry, N. J. and Renner, R · 2011
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Sandwiched rényi divergence satisfies data processing inequality
Beigi, S · 2013
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Breaking the curse of space explosion: Towards efficient nas with curriculum search
Guo, Y., Chen, Y., Zheng, Y., Zhao, P., Chen, J., Huang, J., and Tan, M · 2020
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Truthfulqa: Measuring how models mimic human falsehoods
Lin, S., Hilton, J., and Evans, O · 2021
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Calibrate before use: Improving few-shot performance of language models
Zhao, Z., Wallace, E., Feng, S., Klein, D., and Singh, S · 2021
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URL https://huggingface.co/datasets/OpenAssistant/oasst1
openassistant, 2022 · 2022
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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
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Large language models can self-improve
Huang, J., Gu, S. S., Hou, L., Wu, Y., Wang, X., Yu, H., and Han, J · 2022
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Efficient test-time model adaptation without forgetting
Niu, S., Wu, J., Zhang, Y., Chen, Y., Zheng, S., Zhao, P., and Tan, M · 2022
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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
Cited alongside, same era.
Challenging big-bench tasks and whether chain-of-thought can solve them
Suzgun, M., Scales, N., Schärli, N., Gehrmann, S., Tay, Y., Chung, H. W., Chowdhery, A., Le, Q. V., Chi, E. H., Zhou, D., et al · 2022
Cited alongside, same era.
Self-instruct: Aligning language model with self generated instructions
Wang, Y., Kordi, Y., Mishra, S., Liu, A., Smith, N. A., Khashabi, D., and Hajishirzi, H · 2022
Cited alongside, same era.
Star: Bootstrapping reasoning with reasoning
Zelikman, E., Wu, Y., Mu, J., and Goodman, N · 2022
Cited alongside, same era.
URL https://openai.com/blog/chatgpt
Chatgpt, 2023 · 2023
Cited alongside, same era.
Köpf, A., Kilcher, Y., von Rütte, D., Anagnostidis, S., Tam, Z.-R., Stevens, K., Barhoum, A., Duc, N. M., Stanley, O., Nagyfi, R., et al · 2023
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Prudent silence or foolish babble? examining large language models’ responses to the unknown
Liu, G., Wang, X., Yuan, L., Chen, Y., and Peng, H · 2023
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Towards stable test-time adaptation in dynamic wild world
Niu, S., Wu, J., Zhang, Y., Wen, Z., Chen, Y., Zhao, P., and Tan, M · 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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Alemohammad, S., Casco-Rodriguez, J., Luzi, L., Humayun, A. I., Babaei, H., LeJeune, D., Siahkoohi, A., and Baraniuk, R. G · 2023
Cited alongside, same era.
Large language models suffer from their own output: An analysis of the self-consuming training loop
Briesch, M., Sobania, D., and Rothlauf, F · 2023
Cited alongside, same era.
Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, March 2023
Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J. E., Stoica, I., and Xing, E. P · 2023
Cited alongside, same era.
Qlora: Efficient finetuning of quantized llms
Dettmers, T., Pagnoni, A., Holtzman, A., and Zettlemoyer, L · 2023
Cited alongside, same era.
X-mark: Towards lossless watermarking through lexical redundancy
Chen, L., Bian, Y., Deng, Y., Li, S., Wu, B., Zhao, P., and Wong, K.-f
Cited in the paper.
Alpagasus: Training a better alpaca with fewer data
Chen, L., Li, S., Yan, J., Wang, H., Gunaratna, K., Yadav, V., Tang, Z., Srinivasan, V., Zhou, T., Huang, H., et al
Cited in the paper.
Self-alignment with instruction backtranslation
Li, X., Yu, P., Zhou, C., Schick, T., Zettlemoyer, L., Levy, O., Weston, J., and Lewis, M
Cited in the paper.
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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Efficient toxic content detection by bootstrapping and distilling large language models
Zhang, J., Wu, Q., Xu, Y., Cao, C., Du, Z., and Psounis, 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
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Lima: Less is more for alignment
Zhou, C., Liu, P., Xu, P., Iyer, S., Sun, J., Mao, Y., Ma, X., Efrat, A., Yu, P., Yu, L., et al · 2023
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