In recent years, instruction tuning has gained increasing attention and
emerged as a crucial technique to enhance the capabilities of Large Language
Models (LLMs). To construct high-quality instruction datasets, many instruction
processing approaches have been proposed, aiming to achieve a delicate balance
between data quantity and data quality. Nevertheless, due to inconsistencies
that persist among various instruction processing methods, there is no standard
open-source instruction processing implementation framework available for the
community, which hinders practitioners from further developing and advancing.
To facilitate instruction processing research and development, we present
EasyInstruct, an easy-to-use instruction processing framework for LLMs, which
modularizes instruction generation, selection, and prompting, while also
considering their combination and interaction. EasyInstruct is publicly
released and actively maintained at this https URL,
along with a running demo App at
this https URL for quick-start, calling for
broader research centered on instruction data.

近年来，指导调整已经引起了越来越多的关注，并成为增强大型语言模型（LLM）功能的关键技术。为了构建高质量的指导数据集，已经提出了许多指导处理方法，旨在实现数据数量和数据质量之间的微妙平衡。然而，由于各种指导处理方法之间存在的不一致性，社区中没有标准的开源指导处理实现框架可用，这妨碍了从业者的进一步开发和进展。为了促进指导处理的研究和开发，我们提供了 EasyInstruct，这是一个易于使用的 LLM 指导处理框架，它将指导生成、选择和提示模块化，同时考虑它们的组合和互动。EasyInstruct 已在 https URL 上公开发布，并配有一个运行中的演示应用程序，用于快速启动，并呼吁更广泛的以指导数据为中心的研究。