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

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arxiv 2504.07439 v1 pith:4HPQUPCS submitted 2025-04-10 cs.IR cs.CL

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

Qi Liu , Haozhe Duan , Yiqun Chen , Quanfeng Lu , Weiwei Sun , Jiaxin Mao This is my paper
classification cs.IR cs.CL
keywords llmsrerankingdocumentframeworkllm4rankingmodelsutilizingeasy-to-use
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 4 Pith papers

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  1. PRISMR: Overcoming Parse Collapse in Multimodal Listwise Ranking via Parameterized Representation Internalization

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  2. SearchArt: Training Long-Horizon Search Agent with Scalable Synthetic and Verified Task

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    SearchArt post-trains Qwen3.5-27B on verification-filtered synthetic search trajectories, scoring 74.39 on BrowseComp-ZH, 70.06 on BrowseComp, and 52.55 on DeepResearch-Bench, competitive with several 200B-700B agents.

  3. DocRetriever: A Plug-and-Play Framework for Multimodal Document Retrieval with Comprehensive Benchmark

    cs.CV 2026-05 unverdicted novelty 5.0

    DocRetriever introduces a framework using layout-aware sparse embeddings for hybrid encoding without OCR and a generalizable reasoning-augmented reranker for few-shot settings, plus the MultiDocR benchmark for evaluation.

  4. Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments

    cs.IR 2026-02 conditional novelty 5.0

    Using a fine-tuned 3B LLM to generate millions of textual relevance labels for App Store search improves the ranker's behavioral/textual Pareto frontier and lifts conversion by 0.24%.