Feb. 13, 2024, 5:49 a.m. | Haoyu Wang Guozheng Ma Ziqiao Meng Zeyu Qin Li Shen Zhong Zhang Bingzhe Wu Liu Liu Yat

cs.CL updates on arXiv.org arxiv.org

Self-alignment is an effective way to reduce the cost of human annotation while ensuring promising model capability. However, most current methods complete the data collection and training steps in a single round, which may overlook the continuously improving ability of self-aligned models. This gives rise to a key query: What if we do multi-time bootstrapping self-alignment? Does this strategy enhance model performance or lead to rapid degradation? In this paper, our pioneering exploration delves into the impact of bootstrapping self-alignment …

alignment annotation bootstrapping capability collection cost cs.ai cs.cl current data data collection human key llms query reduce scaling training via

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