LLMBox: A Comprehensive Library for Large Language Models

为了促进大型语言模型（LLMs）的研究，本文提出了一个全面统一的库LLMBox，以便于LLMs的开发、使用和评估。该库具有三个主要优点：（1）统一的数据接口，支持各种训练策略的灵活实现，（2）全面的评估，涵盖广泛的任务、数据集和模型，（3）更多实际考虑，特别是用户友好性和效率。使用我们的库，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。为了严格测试LLMBox，我们在各种评估设置下进行了广泛的实验，实验结果证明了我们的库在支持与LLMs相关的各种实现方面的有效性和效率。详细的介绍和使用指南可以在https://github.com/RUCAIBox/LLMBox找到。

LLMBox: A Comprehensive and Unified Library for Large Language Models

LLMBox provides a unified data interface to facilitate the development, use, and evaluation of LLMs. It also offers a comprehensive evaluation covering extensive tasks, datasets, and models, and focuses on user-friendliness and efficiency.

LLMBox allows for easy reproduction of existing methods, training of new models, and comprehensive performance comparisons. The library was rigorously tested through extensive experiments in diverse evaluation settings, demonstrating its effectiveness and efficiency. The library is open-source and available on GitHub.

Recent related studies in this field include GPT-3, Megatron, and T5.