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.

翻译：为促进大语言模型（LLM）的研究，本文提出了一个全面且统一的库——LLMBox，以简化LLM的开发、使用与评估。该库具备三大主要特点：（1）统一的数据接口，支持灵活实现多种训练策略；（2）涵盖广泛任务、数据集与模型的综合评估体系；（3）更强的实用性考量，尤其在用户友好性与效率方面。借助本库，用户可以轻松复现现有方法、训练新模型并进行全面的性能比较。为严格测试LLMBox，我们在多样化的评估设置中开展了大量实验，结果表明本库在支持各类LLM相关实现方面具有显著的有效性与高效性。详细介绍与使用指南请访问 https://github.com/RUCAIBox/LLMBox。