@inproceedings{ou-etal-2024-easyinstruct,
title = "{E}asy{I}nstruct: An Easy-to-use Instruction Processing Framework for Large Language Models",
author = "Ou, Yixin and
Zhang, Ningyu and
Gui, Honghao and
Xu, Ziwen and
Qiao, Shuofei and
Fang, Runnan and
Li, Lei and
Bi, Zhen and
Zheng, Guozhou and
Chen, Huajun",
editor = "Cao, Yixin and
Feng, Yang and
Xiong, Deyi",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-demos.10",
pages = "94--106",
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 Github, 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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<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 Github, along with an online demo app and a demo video for quick-start, calling for broader research centered on instruction data and synthetic data.</abstract>
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%0 Conference Proceedings
%T EasyInstruct: An Easy-to-use Instruction Processing Framework for Large Language Models
%A Ou, Yixin
%A Zhang, Ningyu
%A Gui, Honghao
%A Xu, Ziwen
%A Qiao, Shuofei
%A Fang, Runnan
%A Li, Lei
%A Bi, Zhen
%A Zheng, Guozhou
%A Chen, Huajun
%Y Cao, Yixin
%Y Feng, Yang
%Y Xiong, Deyi
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F ou-etal-2024-easyinstruct
%X 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 Github, along with an online demo app and a demo video for quick-start, calling for broader research centered on instruction data and synthetic data.
%U https://aclanthology.org/2024.acl-demos.10
%P 94-106
Markdown (Informal)
[EasyInstruct: An Easy-to-use Instruction Processing Framework for Large Language Models](https://aclanthology.org/2024.acl-demos.10) (Ou et al., ACL 2024)
ACL
- Yixin Ou, Ningyu Zhang, Honghao Gui, Ziwen Xu, Shuofei Qiao, Runnan Fang, Lei Li, Zhen Bi, Guozhou Zheng, and Huajun Chen. 2024. EasyInstruct: An Easy-to-use Instruction Processing Framework for Large Language Models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 94–106, Bangkok, Thailand. Association for Computational Linguistics.