LLM4Ranking: An Easy-to-use Framework of Utilizing Large Language Models for Document Reranking

Understand

Utilizing large language models (LLMs) for document reranking has been a popular and promising research direction in recent years, many studies are dedicated to improving the performance and efficiency of using LLMs for reranking.

• Besides, it can also be applied in many real-world applications, such as search engines or retrieval-augmented generation.
• In response to the growing demand for research and application in practice, we introduce a unified framework, \textbf{LLM4Ranking}, which enables users to adopt different ranking methods using open-source or closed-source API-based LLMs.
• Our framework provides a simple and extensible interface for document reranking with LLMs, as well as easy-to-use evaluation and fine-tuning scripts for this task.