FinRAGBench-V: A Benchmark for Multimodal RAG with Visual Citation in the Financial Domain

• URL: http://arxiv.org/abs/2505.17471v1
• Date: Fri, 23 May 2025 04:51:51 GMT
• Title: FinRAGBench-V: A Benchmark for Multimodal RAG with Visual Citation in the Financial Domain
• Authors: Suifeng Zhao, Zhuoran Jin, Sujian Li, Jun Gao,
• Abstract summary: FinRAGBench-V is a comprehensive visual RAG benchmark tailored for finance.<n>It integrates multimodal data and provides visual citation to ensure traceability.<n>RGenCite is an RAG baseline that seamlessly integrates visual citation with generation.
• Score: 17.149559760539344
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retrieval-Augmented Generation (RAG) plays a vital role in the financial domain, powering applications such as real-time market analysis, trend forecasting, and interest rate computation. However, most existing RAG research in finance focuses predominantly on textual data, overlooking the rich visual content in financial documents, resulting in the loss of key analytical insights. To bridge this gap, we present FinRAGBench-V, a comprehensive visual RAG benchmark tailored for finance which effectively integrates multimodal data and provides visual citation to ensure traceability. It includes a bilingual retrieval corpus with 60,780 Chinese and 51,219 English pages, along with a high-quality, human-annotated question-answering (QA) dataset spanning heterogeneous data types and seven question categories. Moreover, we introduce RGenCite, an RAG baseline that seamlessly integrates visual citation with generation. Furthermore, we propose an automatic citation evaluation method to systematically assess the visual citation capabilities of Multimodal Large Language Models (MLLMs). Extensive experiments on RGenCite underscore the challenging nature of FinRAGBench-V, providing valuable insights for the development of multimodal RAG systems in finance.

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