mlsanityData to 14 Oct 2026

Improving Retrieval for RAG based Question Answering Models on Financial Documents

arXiv 2404.07221 ↗First posted 23 Mar 2024cs.IR cs.CL cs.LGcited by 49

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

The effectiveness of Large Language Models (LLMs) in generating accurate responses relies heavily on the quality of input provided, particularly when employing Retrieval Augmented Generation (RAG) techniques. RAG enhances LLMs by sourcing the most relevant text chunk(s) to base queries upon. Despite the significant advancements in LLMs' response quality in recent years, users may still encounter inaccuracies or irrelevant answers; these issues often stem from suboptimal text chunk retrieval by RAG rather than the inherent capabilities of LLMs. To augment the efficacy of LLMs, it is crucial to refine the RAG process. This paper explores the existing constraints of RAG pipelines and introduces methodologies for enhancing text retrieval. It delves into strategies such as sophisticated chunking techniques, query expansion, the incorporation of metadata annotations, the application of re-ranking algorithms, and the fine-tuning of embedding algorithms. Implementing these approaches can substantially improve the retrieval quality, thereby elevating the overall performance and reliability of LLMs in processing and responding to queries.

Key facts. Cited by 49 papers in the map (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 4 of its 7 references that are in the map. First posted on arXiv 23 Mar 2024.

Builds on

4 of 7 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Retrieval-Augmented Generation for Large Language Models: A Survey18 Dec

Other references · 3

  1. 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 517
  2. 2023Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMscited by 178
  3. 2024Financial Report Chunking for Effective Retrieval Augmented Generationcited by 46

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 7; 2025: 27; 2026: 15.

49 papers.

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  1. 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generationcited by 36
  2. 2025KaLM-Embedding-V2: Superior Training Techniques and Data Inspire A Versatile Embedding Modelcited by 36
  3. 2024Toolshed: Scale Tool-Equipped Agents with Advanced RAG-Tool Fusion and Tool Knowledge Basescited by 23
  4. 2025FinSage: A Multi-aspect RAG System for Financial Filings Question Answeringcited by 19
  5. 2025Silent Leaks: Implicit Knowledge Extraction Attack on RAG Systems through Benign Queriescited by 19
  6. 2025FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generationcited by 17
  7. 2025MarketSenseAI 2.0: Enhancing Stock Analysis through LLM Agentscited by 16
  8. 2024FACTS About Building Retrieval Augmented Generation-based Chatbotscited by 15
  9. 2025Graph RAG-Tool Fusioncited by 15
  10. 2024Multi-Reranker: Maximizing performance of retrieval-augmented generation in the FinanceRAG challengecited by 10
  11. 2024Understanding the Impact of Confidence in Retrieval Augmented Generation: A Case Study in the Medical Domaincited by 9
  12. 2025ASRank: Zero-Shot Re-Ranking with Answer Scent for Document Retrievalcited by 9
37 more
  1. 2025Systematic Knowledge Injection into Large Language Models via Diverse Augmentation for Domain-Specific RAGcited by 9
  2. 2025Optimizing Retrieval Strategies for Financial Question Answering Documents in Retrieval-Augmented Generation Systemscited by 9
  3. 2025ReMindRAG: Low-Cost LLM-Guided Knowledge Graph Traversal for Efficient RAGcited by 8
  4. 2025An automatic patent literature retrieval system based on LLM-RAGcited by 7
  5. 2025Towards Context-Robust LLMs: A Gated Representation Fine-tuning Approachcited by 6
  6. 2025RAGVA: Engineering Retrieval Augmented Generation-based Virtual Assistants in Practicecited by 6
  7. 2025Hybrid-NL2SVA: Integrating RAG and Finetuning for LLM-based NL2SVAcited by 6
  8. 2025Parametric Retrieval-Augmented Generation using Latent Routing of LoRA Adapterscited by 6
  9. 2025SustainableQA: A Comprehensive Question Answering Dataset for Corporate Sustainability and EU Taxonomy Reportingcited by 5
  10. 2025Token-Level Precise Attack on RAG: Searching for the Best Alternatives to Mislead Generationcited by 5
  11. 2025Comparison of Text-Based and Image-Based Retrieval in Multimodal Retrieval Augmented Generation Large Language Model Systemscited by 5
  12. 2025From Topology to Retrieval: Decoding Embedding Spaces with Unified Signaturescited by 5
  13. 2025Towards Transparent RAG: Fostering Evidence Traceability in LLM Generation via Reinforcement Learningcited by 4
  14. 2025MAD-Fact: A Multi-Agent Debate Framework for Long-Form Factuality Evaluation in LLMscited by 4
  15. 2025UniC-RAG: Universal Knowledge Corruption Attacks to Retrieval-Augmented Generationcited by 3
  16. 2024KaPQA: Knowledge-Augmented Product Question-Answeringcited by 2
  17. 2025KinyaColBERT: A Lexically Grounded Retrieval Model for Low-Resource Retrieval-Augmented Generationcited by 2
  18. 2025Adapting Large Language Models to Emerging Cybersecurity using Retrieval Augmented Generationcited by 2
  19. 2025Rethinking Retrieval: From Traditional Retrieval Augmented Generation to Agentic and Non-Vector Reasoning Systems in the Financial Domain for Large Language Modelscited by 2
  20. 2025A Knowledge Graph and a Tripartite Evaluation Framework Make Retrieval-Augmented Generation Scalable and Transparentcited by 1
  21. 2026Benchmarking Small Language Models and Small Reasoning Language Models on System Log Severity Classificationcited by 1
  22. 2026Bypassing Document Ingestion: An MCP Approach to Financial Q&Acited by 1
  23. 2026Enhancing LLM Metacognition via Cognitive Pairwise Trainingcited by 1
  24. 2026DKL: Decoupled Knowledge Learning for Instruction-Tuned Language Modelscited by 1
  25. 2026STAIR (STructure Aware Information Retriever): A novel dataset and LLM based retriever for document structure augmentationcited by 1
  26. 2024MANTA -- Model Adapter Native generations that's Affordablecited by 0
  27. 2024PersianRAG: A Retrieval-Augmented Generation System for Persian Languagecited by 0
  28. 2026A Human-Centred Architecture for Large Language Models-Cognitive Assistants in Manufacturing within Quality Management Systemscited by 0
  29. 2026Fast and Faithful: Real-Time Verification for Long-Document Retrieval-Augmented Generation Systemscited by 0
  30. 2026Privacy Without Losing Place: A Paradigm for Private Retrieval in Spatial RAGscited by 0
  31. 2026LeakDojo: Decoding the Leakage Threats of RAG Systemscited by 0
  32. 2026MimirRAG: A Multi-Agent RAG Framework for Financial Data Retrieval with Metadata Integrationcited by 0
  33. 2026MITRE-SAGE: A Multi-Agent Cybersecurity Question-Answering Modelcited by 0
  34. 2026Evaluating Modern RAG: Textual, Multimodal, Dense, and Late Interaction Pipelinescited by 0
  35. 2026Beyond Vector Search: Comparing Classical RAG with Hybrid GraphRAG for Climate Science Q&Acited by 0
  36. 2026IGT @ FinMMEval 2026 Task 2: Question-Type Prompting with Targeted Extraction for Multilingual Financial QAcited by 0
  37. 2026Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoningcited by 0

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  1. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions
  2. 2024ARAGOG: Advanced RAG Output Grading
  3. 2025A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directions
  4. 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey
  5. 2024Meta Knowledge for Retrieval Augmented Large Language Models
  6. 2024A Survey on Retrieval-Augmented Text Generation for Large Language Models
  7. 2024CoFE-RAG: A Comprehensive Full-chain Evaluation Framework for Retrieval-Augmented Generation with Enhanced Data Diversity
  8. 2025Metadata-Driven Retrieval-Augmented Generation for Financial Question Answering