EasyInstruct: An Easy-to-use Instruction Processing Framework for Large Language Models

• URL: http://arxiv.org/abs/2402.03049v4
• Date: Mon, 24 Jun 2024 02:10:23 GMT
• Title: EasyInstruct: An Easy-to-use Instruction Processing Framework for Large Language Models
• Authors: Yixin Ou, Ningyu Zhang, Honghao Gui, Ziwen Xu, Shuofei Qiao, Yida Xue, Runnan Fang, Kangwei Liu, Lei Li, Zhen Bi, Guozhou Zheng, Huajun Chen,
• Abstract summary: EasyInstruct is an easy-to-use instruction processing framework for Large Language Models (LLMs) EasyInstruct modularizes instruction generation, selection, and prompting, while also considering their combination and interaction.
• Score: 37.80143756214926
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 https://github.com/zjunlp/EasyInstruct, along with an online demo app and a demo video for quick-start, calling for broader research centered on instruction data and synthetic data.

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