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

2025-04-10

AI Summary

Large language models (LLMs) face significant performance, efficiency, and engineering bottlenecks in document re-ranking. Method: This paper introduces the first modular, lightweight, plug-and-play LLM re-ranker framework, unifying support for both open-source (Hugging Face) and API-based (e.g., OpenAI) models. It integrates prompt engineering, pairwise/pointwise scoring, LoRA-based efficient fine-tuning, and multi-granularity evaluation (e.g., NDCG, MAP), enabling zero-code model and strategy switching. Contribution/Results: We propose a standardized evaluation protocol and reproducible experimental benchmark, validating over 10 LLMs on MSMARCO and TREC-DL—consistently outperforming traditional baselines. The open-source implementation has been widely adopted, filling a critical gap in practical LLM-based re-ranking tooling.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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, extbf{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.

Problem

• Develops a framework for document reranking using LLMs
• Enables evaluation of different LLM-based ranking methods
• Provides tools for easy implementation and fine-tuning

Innovation

• Unified framework for LLM document reranking
• Supports open-source and API-based LLMs
• Provides evaluation and fine-tuning scripts