LLM4Ranking: An Easy-to-use Framework of Utilizing Large Language Models for Document Reranking
This work addresses the need for practical tools in research and applications like search engines, but it is incremental as it builds on existing methods for using LLMs in reranking.
The authors tackled the problem of document reranking by introducing LLM4Ranking, a unified framework that enables easy adoption of different ranking methods using open-source or closed-source LLMs, and they conducted experiments on widely used datasets to provide reproducible results.
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. We conducted experiments based on this framework and evaluated various models and methods on several widely used datasets, providing reproducibility results on utilizing LLMs for document reranking. Our code is publicly available at https://github.com/liuqi6777/llm4ranking.