mlsanityData to 14 Oct 2026

FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation

arXiv 2504.15800 ↗First posted 22 Apr 2025cs.IRcited by 17

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

Abstract (arXiv)

In the fast-paced financial domain, accurate and up-to-date information is critical to addressing ever-evolving market conditions. Retrieving this information correctly is essential in financial Question-Answering (QA), since many language models struggle with factual accuracy in this domain. We present FinDER, an expert-generated dataset tailored for Retrieval-Augmented Generation (RAG) in finance. Unlike existing QA datasets that provide predefined contexts and rely on relatively clear and straightforward queries, FinDER focuses on annotating search-relevant evidence by domain experts, offering 5,703 query-evidence-answer triplets derived from real-world financial inquiries. These queries frequently include abbreviations, acronyms, and concise expressions, capturing the brevity and ambiguity common in the realistic search behavior of professionals. By challenging models to retrieve relevant information from large corpora rather than relying on readily determined contexts, FinDER offers a more realistic benchmark for evaluating RAG systems. We further present a comprehensive evaluation of multiple state-of-the-art retrieval models and Large Language Models, showcasing challenges derived from a realistic benchmark to drive future research on truthful and precise RAG in the financial domain.

Key facts. Cited by 17 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 42 of its 65 references that are in the map. First posted on arXiv 22 Apr 2025.

Builds on

42 of 65 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024HybridRAG: Integrating Knowledge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extraction9 Aug
  2. 2023Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models6 Oct

Other references · 40

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  3. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  4. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  5. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
  6. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,601
  7. 2024A Survey on LLM-as-a-Judgecited by 1,423
  8. 2023Towards General Text Embeddings with Multi-stage Contrastive Learningcited by 723
  9. 2021FinQA: A Dataset of Numerical Reasoning over Financial Datacited by 562
  10. 2023Active Retrieval Augmented Generationcited by 516
  11. 2022Large Language Models Struggle to Learn Long-Tail Knowledgecited by 458
  12. 2023Ragas: Automated Evaluation of Retrieval Augmented Generationcited by 418
28 more
  1. 2021TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Financecited by 408
  2. 2024Multilingual E5 Text Embeddings: A Technical Reportcited by 372
  3. 2023A Survey of Hallucination in Large Foundation Modelscited by 368
  4. 2023Improving Text Embeddings with Large Language Modelscited by 303
  5. 2023FinanceBench: A New Benchmark for Financial Question Answeringcited by 270
  6. 2019Document Expansion by Query Predictioncited by 231
  7. 2022ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answeringcited by 196
  8. 2023Head-to-Tail: How Knowledgeable are Large Language Models (LLMs)? A.K.A. Will LLMs Replace Knowledge Graphs?cited by 168
  9. 2023Query2doc: Query Expansion with Large Language Modelscited by 149
  10. 2024RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generationcited by 147
  11. 2022MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Datacited by 121
  12. 2024Retrieval Augmented Generation or Long-Context LLMs? A Comprehensive Study and Hybrid Approachcited by 119
  13. 2024Limitations of the LLM-as-a-Judge Approach for Evaluating LLM Outputs in Expert Knowledge Taskscited by 106
  14. 2023Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD Examplescited by 84
  15. 2024Beyond the Limits: A Survey of Techniques to Extend the Context Length in Large Language Modelscited by 76
  16. 2024DocFinQA: A Long-Context Financial Reasoning Datasetcited by 60
  17. 2024FinTextQA: A Dataset for Long-form Financial Question Answeringcited by 54
  18. 2024Improving Retrieval for RAG based Question Answering Models on Financial Documentscited by 49
  19. 2024Financial Report Chunking for Effective Retrieval Augmented Generationcited by 46
  20. 2024The Chronicles of RAG: The Retriever, the Chunk and the Generatorcited by 34
  21. 2024Long Context vs. RAG for LLMs: An Evaluation and Revisitscited by 26
  22. 2024ChunkRAG: Novel LLM-Chunk Filtering Method for RAG Systemscited by 23
  23. 2025Enhancing Retrieval-Augmented Generation: A Study of Best Practicescited by 23
  24. 2023Dynamic Datasets and Market Environments for Financial Reinforcement Learningcited by 15
  25. 2023Large Language Models are legal but they are not: Making the case for a powerful LegalLLMcited by 12
  26. 2023Towards reducing hallucination in extracting information from financial reports using Large Language Modelscited by 11
  27. 2024Do LLMs Really Adapt to Domains? An Ontology Learning Perspectivecited by 11
  28. 2019Managing the Complexity of Processing Financial Data at Scale -- an Experience Reportcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 14.

17 papers.

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  1. 2026Enhancing Financial Report Question-Answering: A Retrieval-Augmented Generation System with Reranking Analysiscited by 21
  2. 2026Sustainable Hybrid Document-Routed Retrieval for Financial RAG: Resolving the Robustness-Precision Trade-offcited by 13
  3. 2026Fin-RATE: A Real-world Financial Analytics and Tracking Evaluation Benchmark for LLMs on SEC Filingscited by 8
  4. 2026BigFinanceBench: A Workflow-Grounded Benchmark for Financial-Research Agentscited by 7
  5. 2026From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discoverycited by 6
  6. 2026Improving the Completeness and Comparability of Segment Disclosures: A Large Language Model Approachcited by 6
  7. 2025Metadata-Driven Retrieval-Augmented Generation for Financial Question Answeringcited by 4
  8. 2026FinRetrieval: A Benchmark for Financial Data Retrieval by AI Agentscited by 4
  9. 2025Enhancing Document-Level Question Answering via Multi-Hop Retrieval-Augmented Generation with LLaMA 3cited by 1
  10. 2026Bypassing Document Ingestion: An MCP Approach to Financial Q&Acited by 1
  11. 2026Evidence-Unit Fairness and the Limits of Query-Adaptive Sparse-Dense Fusion in Financial Document Retrievalcited by 1
  12. 2026FinRank: An Evidence-Grounded Benchmark for Financial Question Answering and Retrieval over SEC Filingscited by 1
5 more
  1. 2025History Rhymes: Macro-Contextual Retrieval for Robust Financial Forecastingcited by 0
  2. 2026MimirRAG: A Multi-Agent RAG Framework for Financial Data Retrieval with Metadata Integrationcited by 0
  3. 2026FinSAgent: Corpus-Aligned Multi-Agent RAG Framework for Evidence-Grounded SEC Filing Question Answeringcited by 0
  4. 2026Enhancing Financial Question Answering: A Novel Benchmark Dataset of Banks' financial statementscited by 0
  5. 2026FinAutoRubric: Expert-Guided Automatic Rubric Generation for Evaluating Financial Research Agentscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025VeritasFi: An Adaptable, Multi-tiered RAG Framework for Multi-modal Financial Question Answering
  2. 2024SEC-QA: A Systematic Evaluation Corpus for Financial QA
  3. 2025Optimizing Retrieval Strategies for Financial Question Answering Documents in Retrieval-Augmented Generation Systems
  4. 2024Multi-Reranker: Maximizing performance of retrieval-augmented generation in the FinanceRAG challenge
  5. 2025FinGEAR: Financial Mapping-Guided Enhanced Answer Retrieval
  6. 2025Rethinking Retrieval: From Traditional Retrieval Augmented Generation to Agentic and Non-Vector Reasoning Systems in the Financial Domain for Large Language Models
  7. 2025FinS-Pilot: A Benchmark for Online Financial RAG System
  8. 2024FinLLMs: A Framework for Financial Reasoning Dataset Generation with Large Language Models