mlsanityData to 14 Oct 2026

Financial Report Chunking for Effective Retrieval Augmented Generation

arXiv 2402.05131 ↗First posted 5 Feb 2024cs.CLcited by 46

On the map: Retrieval-augmented generation → Multimodal RAG

Abstract (arXiv)

Chunking information is a key step in Retrieval Augmented Generation (RAG). Current research primarily centers on paragraph-level chunking. This approach treats all texts as equal and neglects the information contained in the structure of documents. We propose an expanded approach to chunk documents by moving beyond mere paragraph-level chunking to chunk primary by structural element components of documents. Dissecting documents into these constituent elements creates a new way to chunk documents that yields the best chunk size without tuning. We introduce a novel framework that evaluates how chunking based on element types annotated by document understanding models contributes to the overall context and accuracy of the information retrieved. We also demonstrate how this approach impacts RAG assisted Question & Answer task performance. Our research includes a comprehensive analysis of various element types, their role in effective information retrieval, and the impact they have on the quality of RAG outputs. Findings support that element type based chunking largely improve RAG results on financial reporting. Through this research, we are also able to answer how to uncover highly accurate RAG.

Key facts. Cited by 46 papers in the map (data through 14 Oct 2026). Placed in Multimodal RAG, one of 15 directions in Retrieval-augmented generation. Builds on 28 of its 46 references that are in the map. First posted on arXiv 5 Feb 2024.

Builds on

28 of 46 references.

References · 28

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  3. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  4. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
  5. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  6. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
  7. 2023BloombergGPT: A Large Language Model for Financecited by 939
  8. 2016Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphscited by 861
  9. 2021FinQA: A Dataset of Numerical Reasoning over Financial Datacited by 562
  10. 2021TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Financecited by 408
  11. 2023FinanceBench: A New Benchmark for Financial Question Answeringcited by 270
  12. 2023Challenges and Applications of Large Language Modelscited by 254
16 more
  1. 2023FinGPT: Open-Source Financial Large Language Modelscited by 253
  2. 2022ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answeringcited by 196
  3. 2022DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysiscited by 148
  4. 2020Global Table Extractor (GTE): A Framework for Joint Table Identification and Cell Structure Recognition Using Visual Contextcited by 147
  5. 2024Seven Failure Points When Engineering a Retrieval Augmented Generation Systemcited by 139
  6. 2022WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domaincited by 117
  7. 2023Are Large Language Model-based Evaluators the Solution to Scaling Up Multilingual Evaluation?cited by 88
  8. 2023Cognitive Mirage: A Review of Hallucinations in Large Language Modelscited by 84
  9. 2023Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Modelscited by 58
  10. 2023Assessing the Quality of Multiple-Choice Questions Using GPT-4 and Rule-Based Methodscited by 24
  11. 2023REFinD: Relation Extraction Financial Datasetcited by 17
  12. 2023ChatGPT's One-year Anniversary: Are Open-Source Large Language Models Catching up?cited by 15
  13. 2023Zero-Shot Question Answering over Financial Documents using Large Language Modelscited by 7
  14. 2023Context Tuning for Retrieval Augmented Generationcited by 6
  15. 2023Sentiment Analysis in Finance: From Transformers Back to eXplainable Lexicons (XLex)cited by 5
  16. 2023Conversational Factor Information Retrieval Model (ConFIRM)cited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 8; 2025: 21; 2026: 17.

46 papers.

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  1. 2024HybridRAG: Integrating Knowledge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extractioncited by 93
  2. 2024A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challengescited by 90
  3. 2024Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wiselycited by 66
  4. 2025ChunkKV: Semantic-Preserving KV Cache Compression for Efficient Long-Context LLM Inferencecited by 60
  5. 2024Improving Retrieval for RAG based Question Answering Models on Financial Documentscited by 49
  6. 2024Ragnar"ok: A Reusable RAG Framework and Baselines for TREC 2024 Retrieval-Augmented Generation Trackcited by 32
  7. 2025AI Hiring with LLMs: A Context-Aware and Explainable Multi-Agent Framework for Resume Screeningcited by 20
  8. 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Surveycited by 20
  9. 2025FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generationcited by 17
  10. 2025CPA-RAG:Covert Poisoning Attacks on Retrieval-Augmented Generation in Large Language Modelscited by 17
  11. 2025MarketSenseAI 2.0: Enhancing Stock Analysis through LLM Agentscited by 16
  12. 2025RAG Meets Temporal Graphs: Time-Sensitive Modeling and Retrieval for Evolving Knowledgecited by 13
34 more
  1. 2025Optimizing Retrieval Strategies for Financial Question Answering Documents in Retrieval-Augmented Generation Systemscited by 9
  2. 2025MultiFinRAG: An Optimized Multimodal Retrieval-Augmented Generation (RAG) Framework for Financial Question Answeringcited by 9
  3. 2025FinSight: Towards Real-World Financial Deep Researchcited by 9
  4. 2025Retrieval-Augmented Purifier for Robust LLM-Empowered Recommendationcited by 7
  5. 2025Chunk Twice, Embed Once: A Systematic Study of Segmentation and Representation Trade-offs in Chemistry-Aware Retrieval-Augmented Generationcited by 6
  6. 2024Enhancing Tourism Recommender Systems for Sustainable City Trips Using Retrieval-Augmented Generationcited by 4
  7. 2024Empowering Meta-Analysis: Leveraging Large Language Models for Scientific Synthesiscited by 4
  8. 2025REIS: A High-Performance and Energy-Efficient Retrieval System with In-Storage Processingcited by 4
  9. 2025Metadata-Driven Retrieval-Augmented Generation for Financial Question Answeringcited by 4
  10. 2026Taxonomy of the Retrieval System Framework: Pitfalls and Paradigmscited by 4
  11. 2024A Scalable Data-Driven Framework for Systematic Analysis of SEC 10-K Filings Using Large Language Modelscited by 3
  12. 2025LLMs for Supply Chain Managementcited by 3
  13. 2026A Systematic Investigation of Document Chunking Strategies and Embedding Sensitivitycited by 3
  14. 2025Fine-Grained Privacy Extraction from Retrieval-Augmented Generation Systems via Knowledge Asymmetry Exploitationcited by 2
  15. 2025CelloAI: Leveraging Large Language Models for HPC Software Development in High Energy Physicscited by 2
  16. 2025An Agile Method for Implementing Retrieval Augmented Generation Tools in Industrial SMEscited by 2
  17. 2025Knowledge-Grounded Agentic Large Language Models for Multi-Hazard Understanding from Reconnaissance Reportscited by 2
  18. 2026Legal-DC: Benchmarking Retrieval-Augmented Generation for Legal Documentscited by 2
  19. 2025TAdaRAG: Task Adaptive Retrieval-Augmented Generation via On-the-Fly Knowledge Graph Constructioncited by 1
  20. 2026Bypassing Document Ingestion: An MCP Approach to Financial Q&Acited by 1
  21. 2026Retrieval-Augmented Question Answering over Scientific Literature for the Electron-Ion Collidercited by 1
  22. 2026FinRED: An Expert-Guided Benchmark Generation and Evaluation Framework for Financial LLM Red-Teamingcited by 1
  23. 2026The Effect of Text Chunk Size on Retrieval-Augmented Generation Performancecited by 1
  24. 2026Bridging the Question-Answer Gap in Retrieval-Augmented Generation: Hypothetical Prompt Embeddingscited by 1
  25. 2025Bridging Language Gaps with Adaptive RAG: Improving Indonesian Language Question Answeringcited by 0
  26. 2026Decomposing Retrieval Failures in RAG for Long-Document Financial Question Answeringcited by 0
  27. 2026MDKeyChunker: Single-Call LLM Enrichment with Rolling Keys and Key-Based Restructuring for High-Accuracy RAGcited by 0
  28. 2026RefineRAG: Word-Level Poisoning Attacks via Retriever-Guided Text Refinementcited by 0
  29. 2026Adaptive Query Routing: A Tier-Based Framework for Hybrid Retrieval Across Financial, Legal, and Medical Documentscited by 0
  30. 2026M3DocDep: Multi-modal, Multi-page, Multi-document Dependency Chunking with Large Vision-Language Modelscited by 0
  31. 2026MimirRAG: A Multi-Agent RAG Framework for Financial Data Retrieval with Metadata Integrationcited by 0
  32. 2026VeriGraph: Towards Verifiable Data-Analytic Agentscited by 0
  33. 2026Evaluating Modern RAG: Textual, Multimodal, Dense, and Late Interaction Pipelinescited by 0
  34. 2026A Training-Free, Alignment-Free Approach to Corporate Intelligence: Application to SEC Filingscited by 0

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  2. 2024Is Semantic Chunking Worth the Computational Cost?
  3. 2026Chunking Methods on Retrieval-Augmented Generation - Effectiveness Evaluation Against Computational Cost and Limitations
  4. 2025A New HOPE: Domain-agnostic Automatic Evaluation of Text Chunking
  5. 2026Empirical Evaluation of PDF Parsing and Chunking for Financial Question Answering with RAG
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