Abstract: Safety alignment is a critical technique for preventing large language models(LLMs) from generating harmful content and ensuring their outputs conform to human values and safety standards,whose effectiveness heavily relies on high-quality datasets.Current research primarily focuses on English contexts and Western cultural backgrounds,lacking specialized datasets for Chinese scenarios.This paper systematically reviews the technical framework of LLMs safety alignment and provides a focused overview of the current landscape of publicly available Chinese safety alignment datasets.Firstly,it clarifies the position of safety alignment within the post-training safety paradigm and its technical evolution.Then,it analyzes the characteristics and limitations of existing Chinese datasets in terms of safety risk taxonomy,data scale,and application purposes.Finally,this paper outlines future research directions from three perspectives:data scale,completeness,and orientation.It suggests that future work should prioritize data quality,mitigate the “safety tax” phenomenon,and explore synergistic optimization between pre-training and alignment stages.This survey aims to provide a data foundation and research direction for building more efficient,robust,and contextually appropriate safety alignment systems for the Chinese language.

Key words: Large language models, Safety alignment, Chinese datasets, Jailbreaking