LLMBox: A Comprehensive Library for Large Language Models

TL;DR

LLMBox is a unified library designed to streamline the development, evaluation, and comparison of large language models, emphasizing user-friendliness, efficiency, and comprehensive task coverage.

Contribution

This paper introduces LLMBox, a comprehensive library that simplifies LLM development and evaluation with a unified interface and extensive task support, enhancing research productivity.

Findings

LLMBox effectively supports diverse LLM training and evaluation tasks.

The library improves efficiency and reproducibility in LLM research.

Experimental results confirm the library's practicality and performance benefits.

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.