ACL 2024system demonstrations2 citations

LLMBox: A Comprehensive Library for Large Language Models

Tianyi Tang, Hu Yiwen, Bingqian Li, Wenyang Luo, ZiJing Qin, Haoxiang Sun, Jiapeng Wang, Shiyi Xu

Abstract

To facilitate the research on large language models (LLMs), this paper presents a comprehensive and unified library, LLMBox, to ease the development, use, and evaluation of LLMs. This library is featured with three main merits: (1) a unified data interface that supports the flexible implementation of various training strategies, (2) a comprehensive evaluation that covers extensive tasks, datasets, and models, and (3) more practical consideration, especially on user-friendliness and efficiency. With our library, users can easily reproduce existing methods, train new models, and conduct comprehensive performance comparisons. To rigorously test LLMBox, we conduct extensive experiments in a diverse coverage of evaluation settings, and experimental results demonstrate the effectiveness and efficiency of our library in supporting various implementations related to LLMs. The detailed introduction and usage guidance can be found at https://github.com/RUCAIBox/LLMBox.

BibTeX
@inproceedings{tang-etal-2024-llmbox,
    title = "{LLMB}ox: A Comprehensive Library for Large Language Models",
    author = "Tang, Tianyi  and
      Yiwen, Hu  and
      Li, Bingqian  and
      Luo, Wenyang  and
      Qin, ZiJing  and
      Sun, Haoxiang  and
      Wang, Jiapeng  and
      Xu, Shiyi  and
      Cheng, Xiaoxue  and
      Guo, Geyang  and
      Peng, Han  and
      Zheng, Bowen  and
      Tang, Yiru  and
      Min, Yingqian  and
      Chen, Yushuo  and
      Chen, Jie  and
      Zhao, Ranchi  and
      Ding, Luran  and
      Wang, Yuhao  and
      Dong, Zican  and
      Chunxuan, Xia  and
      Li, Junyi  and
      Zhou, Kun  and
      Zhao, Xin  and
      Wen, Ji-Rong",
    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.37/",
    doi = "10.18653/v1/2024.acl-demos.37",
    pages = "388--399"
}
LLMBox: A Comprehensive Library for Large Language Models · ACL 2024