@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",
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 \url{https://github.com/RUCAIBox/LLMBox}.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T LLMBox: A Comprehensive Library for Large Language Models
%A Tang, Tianyi
%A Yiwen, Hu
%A Li, Bingqian
%A Luo, Wenyang
%A Qin, ZiJing
%A Sun, Haoxiang
%A Wang, Jiapeng
%A Xu, Shiyi
%A Cheng, Xiaoxue
%A Guo, Geyang
%A Peng, Han
%A Zheng, Bowen
%A Tang, Yiru
%A Min, Yingqian
%A Chen, Yushuo
%A Chen, Jie
%A Zhao, Ranchi
%A Ding, Luran
%A Wang, Yuhao
%A Dong, Zican
%A Chunxuan, Xia
%A Li, Junyi
%A Zhou, Kun
%A Zhao, Xin
%A Wen, Ji-Rong
%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 tang-etal-2024-llmbox
%X 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.
%R 10.18653/v1/2024.acl-demos.37
%U https://aclanthology.org/2024.acl-demos.37
%U https://doi.org/10.18653/v1/2024.acl-demos.37
%P 388-399
Markdown (Informal)
[LLMBox: A Comprehensive Library for Large Language Models](https://aclanthology.org/2024.acl-demos.37) (Tang et al., ACL 2024)
ACL
- Tianyi Tang, Hu Yiwen, Bingqian Li, Wenyang Luo, ZiJing Qin, Haoxiang Sun, Jiapeng Wang, Shiyi Xu, Xiaoxue Cheng, Geyang Guo, Han Peng, Bowen Zheng, Yiru Tang, Yingqian Min, Yushuo Chen, Jie Chen, Ranchi Zhao, Luran Ding, Yuhao Wang, et al.. 2024. LLMBox: A Comprehensive Library for Large Language Models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 388–399, Bangkok, Thailand. Association for Computational Linguistics.