LLM4Ranking: An Easy-to-use Framework of Utilizing Large Language Models for Document Reranking
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866913787353759744 |
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| author | Liu, Qi Duan, Haozhe Chen, Yiqun Lu, Quanfeng Sun, Weiwei Mao, Jiaxin |
| author_facet | Liu, Qi Duan, Haozhe Chen, Yiqun Lu, Quanfeng Sun, Weiwei Mao, Jiaxin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_07439 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LLM4Ranking: An Easy-to-use Framework of Utilizing Large Language Models for Document Reranking Liu, Qi Duan, Haozhe Chen, Yiqun Lu, Quanfeng Sun, Weiwei Mao, Jiaxin Information Retrieval Computation and Language 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. |
| title | LLM4Ranking: An Easy-to-use Framework of Utilizing Large Language Models for Document Reranking |
| topic | Information Retrieval Computation and Language |
| url | https://arxiv.org/abs/2504.07439 |