EasyInstruct: An Easy-to-use Instruction Processing Framework for Large Language Models
<|reference_start|>EasyInstruct: An Easy-to-use Instruction Processing Framework for Large Language Models: 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.<|reference_end|>
@article{ou2024easyinstruct:,
    title={EasyInstruct: An Easy-to-use Instruction Processing Framework for Large
  Language Models},
    author={Yixin Ou, Ningyu Zhang, Honghao Gui, Ziwen Xu, Shuofei Qiao, Yida Xue,
  Runnan Fang, Kangwei Liu, Lei Li, Zhen Bi, Guozhou Zheng, Huajun Chen},
    journal={arXiv preprint arXiv:2402.03049},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.03049},
    primaryClass={cs.CL cs.AI cs.HC cs.IR cs.LG}
}