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Step-On-Feet Tuning: Scaling Self-Alignment of LLMs via Bootstrapping

Self-alignment is an effective way to reduce the cost of human annotation while ensuring promising model capability. This objective can be achieved from three aspects: (i) high quality data generation aspect, (ii) ready-made data utilizing aspect, (iii) model internal capacity utilizing aspect.

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Cite this as

Haoyu Wang, Guozheng Ma, Ziqiao Meng, Zeyu Qin, Li Shen, Zhong Zhang, Bingzhe Wu, Liu Liu, Yatao Bian, Tingyang Xu, Xueqian Wang, Peilin Zhao (2024). Dataset: Step-On-Feet Tuning: Scaling Self-Alignment of LLMs via Bootstrapping. https://doi.org/10.57702/geepzxt9

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Additional Info

Field	Value
Created	December 16, 2024
Last update	December 16, 2024
Defined In	https://doi.org/10.48550/arXiv.2402.07610
Author	Haoyu Wang
More Authors	Guozheng Ma Ziqiao Meng Zeyu Qin Li Shen Zhong Zhang Bingzhe Wu Liu Liu Yatao Bian Tingyang Xu Xueqian Wang Peilin Zhao