mmRAG: A Modular Benchmark for Retrieval-Augmented Generation over Text, Tables, and Knowledge Graphs

• URL: http://arxiv.org/abs/2505.11180v1
• Date: Fri, 16 May 2025 12:31:29 GMT
• Title: mmRAG: A Modular Benchmark for Retrieval-Augmented Generation over Text, Tables, and Knowledge Graphs
• Authors: Chuan Xu, Qiaosheng Chen, Yutong Feng, Gong Cheng,
• Abstract summary: We introduce mmRAG, a modular benchmark for evaluating multi-modal RAG systems.<n>Our benchmark integrates queries from six diverse question-answering datasets spanning text, tables, and knowledge graphs.<n>We follow standard information retrieval procedures to annotate document relevance and derive dataset relevance.
• Score: 11.861763118322136
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for enhancing the capabilities of large language models. However, existing RAG evaluation predominantly focuses on text retrieval and relies on opaque, end-to-end assessments of generated outputs. To address these limitations, we introduce mmRAG, a modular benchmark designed for evaluating multi-modal RAG systems. Our benchmark integrates queries from six diverse question-answering datasets spanning text, tables, and knowledge graphs, which we uniformly convert into retrievable documents. To enable direct, granular evaluation of individual RAG components -- such as the accuracy of retrieval and query routing -- beyond end-to-end generation quality, we follow standard information retrieval procedures to annotate document relevance and derive dataset relevance. We establish baseline performance by evaluating a wide range of RAG implementations on mmRAG.

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