大语言模型指令微调的数据选择综述
A Survey on Data Selection for LLM Instruction Tuning
AI总结：
本文综述大语言模型（LLMs）指令微调的数据选择研究，介绍常用指令数据集，提出数据选择方法新分类体系并详述进展、评估策略，指出挑战与前沿方向，助力提升LLMs指令遵循能力并降低训练成本。
摘要：
指令微调是训练大语言模型（LLMs）的关键步骤，因此如何提升指令微调效果受到越来越多的关注。现有研究表明，在LLMs指令微调过程中，数据集质量比数量更重要。因此，近期大量研究聚焦于从指令数据集中选择高质量子集的方法，旨在降低训练成本并增强LLMs的指令遵循能力。本文对LLMs指令微调的数据选择进行了全面综述：首先介绍了广泛使用的指令数据集；然后提出了数据选择方法的新分类体系，详细介绍了最新进展，还阐述了评估策略与结果；最后强调了开放挑战并提出该任务的新前沿方向。
英文摘要：
Instruction tuning is a vital step of training large language models (LLMs), 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 LLMs. 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
