mlsanityData to 14 Oct 2026

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

arXiv 2505.17471 ↗First posted 23 May 2025cs.CLcited by 16

On the map: Retrieval-augmented generation → Legal and financial RAG

Abstract (arXiv)

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.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). Placed in Legal and financial RAG, one of 15 directions in Retrieval-augmented generation. Builds on 25 of its 36 references that are in the map. First posted on arXiv 23 May 2025.

Builds on

25 of 36 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024OmniEval: An Omnidirectional and Automatic RAG Evaluation Benchmark in Financial Domain17 Dec

Other references · 24

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2024Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolutioncited by 4,234
  3. 2024Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextcited by 3,224
  4. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  5. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
  6. 2024MiniCPM-V: A GPT-4V Level MLLM on Your Phonecited by 1,079
  7. 2024M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillationcited by 1,073
  8. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 952
  9. 2023Enabling Large Language Models to Generate Text with Citationscited by 595
  10. 2021FinQA: A Dataset of Numerical Reasoning over Financial Datacited by 562
  11. 2023ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systemscited by 265
  12. 2024RAFT: Adapting Language Model to Domain Specific RAGcited by 262
12 more
  1. 2024RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMscited by 202
  2. 2024ColPali: Efficient Document Retrieval with Vision Language Modelscited by 195
  3. 2024GME: Improving Universal Multimodal Retrieval by Multimodal LLMscited by 170
  4. 2024RAGBench: Explainable Benchmark for Retrieval-Augmented Generation Systemscited by 117
  5. 2024Unifying Multimodal Retrieval via Document Screenshot Embeddingcited by 110
  6. 2023A Survey of Large Language Models Attributioncited by 71
  7. 2024Attribute First, then Generate: Locally-attributable Grounded Text Generationcited by 54
  8. 2024RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answeringcited by 43
  9. 2023RAVEN: In-Context Learning with Retrieval-Augmented Encoder-Decoder Language Modelscited by 37
  10. 2024EvoChart: A Benchmark and a Self-Training Approach Towards Real-World Chart Understandingcited by 34
  11. 2024VISA: Retrieval Augmented Generation with Visual Source Attributioncited by 28
  12. 2024Jina-ColBERT-v2: A General-Purpose Multilingual Late Interaction Retrievercited by 25

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 12.

16 papers.

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  1. 2025DocLens : A Tool-Augmented Multi-Agent Framework for Long Visual Document Understandingcited by 15
  2. 2025Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understandingcited by 9
  3. 2026AgenticOCR: Parsing Only What You Need for Efficient Retrieval-Augmented Generationcited by 5
  4. 2026CiteVQA: Benchmarking Evidence Attribution for Trustworthy Document Intelligencecited by 5
  5. 2025VeritasFi: An Adaptable, Multi-tiered RAG Framework for Multi-modal Financial Question Answeringcited by 3
  6. 2026MM-BizRAG: Rethinking Multimodal Retrieval-Augmented Generation for General Purpose Enterprise Q&Acited by 1
  7. 2026FinRank: An Evidence-Grounded Benchmark for Financial Question Answering and Retrieval over SEC Filingscited by 1
  8. 2025PyFi: Toward Pyramid-like Financial Image Understanding for VLMs via Adversarial Agentscited by 0
  9. 2026LITTA: Late-Interaction and Test-Time Alignment for Visually-Grounded Multimodal Retrievalcited by 0
  10. 2026FinSAgent: Corpus-Aligned Multi-Agent RAG Framework for Evidence-Grounded SEC Filing Question Answeringcited by 0
  11. 2026VLD-RAG: Agentic Vision-Language Retrieval-Augmented Generation for Long, Visually-Rich Multi-Page Documentscited by 0
  12. 2026XL-DocBench: Benchmarking Evidence-Grounded Extra-Long Document Understandingcited by 0
4 more
  1. 2026DocAtlas: Long-Document Understanding as Mutable-State Interactioncited by 0
  2. 2026CLAIR-Fin: An Adversarial Multi-Agent Framework for Claim-Level Verification and Adaptive Debate in Cross-Modal Financial QAcited by 0
  3. 2026Incremental Pooled LLM Evaluation for Cost-Effective Retrieval Model Selectioncited by 0
  4. 2026IGT @ FinMMEval 2026 Task 2: Question-Type Prompting with Targeted Extraction for Multilingual Financial QAcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Towards Temporal-Aware Multi-Modal Retrieval Augmented Generation in Finance
  2. 2025MultiFinRAG: An Optimized Multimodal Retrieval-Augmented Generation (RAG) Framework for Financial Question Answering
  3. 2025FinS-Pilot: A Benchmark for Online Financial RAG System
  4. 2024Multi-Reranker: Maximizing performance of retrieval-augmented generation in the FinanceRAG challenge
  5. 2025CFBenchmark-MM: Chinese Financial Assistant Benchmark for Multimodal Large Language Model
  6. 2025Metadata-Driven Retrieval-Augmented Generation for Financial Question Answering
  7. 2025Rethinking Retrieval: From Traditional Retrieval Augmented Generation to Agentic and Non-Vector Reasoning Systems in the Financial Domain for Large Language Models
  8. 2026From BM25 to Corrective RAG: Benchmarking Retrieval Strategies for Text-and-Table Documents