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RAG-NIMBF: A Non-Invasive, Modular Benchmarking Framework for Automated RAG Configuration Analysis

Niclas Hart, Nicolas Weeger · 2026 · arXiv

DOI: 10.48550/arXiv.2610.27816 · open-access PDF

Chinese

Retrieval-Augmented Generation(RAG)已成为在外部知识中为大型语言模型(LLM)的输出奠定基础的主要范式,然而,从业人员面临着一个广泛的配置空间,包括分解,嵌入,检索,重新排列,提示和生成模型,并且对这些组件如何相互作用的指导有限。我们介绍了RAG-NIMBF,一个模块化基准框架,它将现有RAG管道作为一个非侵入性的评估层连接起来。

Machine translation (Meta M2M100); the English original follows.

Abstract

Retrieval-Augmented Generation (RAG) has become the dominant paradigm for grounding the outputs of large language models (LLM) in external knowledge. However, practitioners are faced with a vast configuration space comprising chunking, embedding, retrieval, reranking, prompting and generation models, and there is limited guidance on how these components interact. We present RAG-NIMBF, a modular benchmarking framework that attaches to existing RAG pipelines as a non-invasive evaluation layer. It evaluates the configuration space through controlled ablation with end-to-end experiment tracking. T

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