  Instruction tuning is a vital step of training large language models (LLM),
so how to enhance the effect of instruction tuning has received increased
attention. Existing works indicate that the quality of the dataset is more
crucial than the quantity during instruction tuning of LLM. Therefore, recently
a lot of studies focus on exploring the methods of selecting high-quality
subset from instruction datasets, aiming to reduce training costs and enhance
the instruction-following capabilities of LLMs. This paper presents a
comprehensive survey on data selection for LLM instruction tuning. Firstly, we
introduce the wildly used instruction datasets. Then, we propose a new taxonomy
of the data selection methods and provide a detailed introduction of recent
advances,and the evaluation strategies and results of data selection methods
are also elaborated in detail. Finally, we emphasize the open challenges and
present new frontiers of this task.
