mlsanityData to 7 Oct 2026

Deficiency of Large Language Models in Finance: An Empirical Examination of Hallucination

arXiv 2311.15548 ↗First posted 27 Nov 2023cs.CL cs.AI cs.LGcited by 41

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Abstract (arXiv)

The hallucination issue is recognized as a fundamental deficiency of large language models (LLMs), especially when applied to fields such as finance, education, and law. Despite the growing concerns, there has been a lack of empirical investigation. In this paper, we provide an empirical examination of LLMs' hallucination behaviors in financial tasks. First, we empirically investigate LLM model's ability of explaining financial concepts and terminologies. Second, we assess LLM models' capacity of querying historical stock prices. Third, to alleviate the hallucination issue, we evaluate the efficacy of four practical methods, including few-shot learning, Decoding by Contrasting Layers (DoLa), the Retrieval Augmentation Generation (RAG) method and the prompt-based tool learning method for a function to generate a query command. Finally, our major finding is that off-the-shelf LLMs experience serious hallucination behaviors in financial tasks. Therefore, there is an urgent need to call for research efforts in mitigating LLMs' hallucination.

Key facts. Cited by 41 papers in the map (data through 7 Oct 2026). Builds on 27 of its 29 references that are in the map. First posted on arXiv 27 Nov 2023.

Builds on

27 of 29 references.

References · 27

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023GPT-4 Technical Reportcited by 18,132
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  4. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  5. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  6. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  7. 2017Billion-scale similarity search with GPUscited by 2,477
  8. 2022Survey of Hallucination in Natural Language Generationcited by 2,056
  9. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
  10. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,582
  11. 2023Gorilla: Large Language Model Connected with Massive APIscited by 1,302
  12. 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,259
15 more
  1. 2023Inference-Time Intervention: Eliciting Truthful Answers from a Language Modelcited by 1,259
  2. 2023BloombergGPT: A Large Language Model for Financecited by 923
  3. 2023Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Modelscited by 695
  4. 2022Contrastive Decoding: Open-ended Text Generation as Optimizationcited by 598
  5. 2023Active Retrieval Augmented Generationcited by 506
  6. 2023DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Modelscited by 367
  7. 2023Trusting Your Evidence: Hallucinate Less with Context-aware Decodingcited by 319
  8. 2023FinGPT: Open-Source Financial Large Language Modelscited by 244
  9. 2023PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Financecited by 217
  10. 2020FinBERT: A Pretrained Language Model for Financial Communicationscited by 161
  11. 2023Med-HALT: Medical Domain Hallucination Test for Large Language Modelscited by 156
  12. 2023Chatlaw: A Multi-Agent Legal Assistant based on a Role-Aligned Mixture-of-Experts Architecturecited by 119
  13. 2023Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Modelscited by 67
  14. 2023Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Modelscited by 58
  15. 2023Halo: Estimation and Reduction of Hallucinations in Open-Source Weak Large Language Modelscited by 36

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 12; 2025: 16; 2026: 13.

41 papers.

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  1. 2025A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deploymentcited by 110
  2. 2024A Comprehensive Survey of Hallucination in Large Language, Image, Video and Audio Foundation Modelscited by 98
  3. 2024A Survey of Large Language Models in Finance (FinLLMs)cited by 77
  4. 2024LLMs Will Always Hallucinate, and We Need to Live With Thiscited by 77
  5. 2025Large Language Models Hallucination: A Comprehensive Surveycited by 45
  6. 2024Confabulation: The Surprising Value of Large Language Model Hallucinationscited by 42
  7. 2024Knowledge Boundary of Large Language Models: A Surveycited by 36
  8. 2024FinTral: A Family of GPT-4 Level Multimodal Financial Large Language Modelscited by 32
  9. 2024Towards Rationality in Language and Multimodal Agents: A Surveycited by 15
  10. 2025Standard Benchmarks Fail -- Auditing LLM Agents in Finance Must Prioritize Riskcited by 13
  11. 2024ERBench: An Entity-Relationship based Automatically Verifiable Hallucination Benchmark for Large Language Modelscited by 9
  12. 2024NoVo: Norm Voting off Hallucinations with Attention Heads in Large Language Modelscited by 9
29 more
  1. 2025FAITH: A Framework for Assessing Intrinsic Tabular Hallucinations in Financecited by 8
  2. 2025FinTrust: A Comprehensive Benchmark of Trustworthiness Evaluation in Finance Domaincited by 8
  3. 2025HACK: Hallucinations Along Certainty and Knowledge Axescited by 6
  4. 2024It Cannot Be Right If It Was Written by AI: On Lawyers' Preferences of Documents Perceived as Authored by an LLM vs a Humancited by 5
  5. 2025IKOD: Mitigating Visual Attention Degradation in Large Vision-Language Modelscited by 5
  6. 2026MARCH: Multi-Agent Reinforced Self-Check for LLM Hallucinationcited by 5
  7. 2025Exploring the Generalizability of Factual Hallucination Mitigation via Enhancing Precise Knowledge Utilizationcited by 4
  8. 2025Bridging Language Models and Financial Analysiscited by 4
  9. 2025Finance Language Model Evaluation (FLaME)cited by 4
  10. 2026FinGround: Detecting and Grounding Financial Hallucinations via Atomic Claim Verificationcited by 4
  11. 2024HALO: Hallucination Analysis and Learning Optimization to Empower LLMs with Retrieval-Augmented Context for Guided Clinical Decision Makingcited by 3
  12. 2026Hallucination Detection and Mitigation in Large Language Modelscited by 3
  13. 2026The Alpha Illusion: Reported Alpha from LLM Trading Agents Should Not Be Treated as Deployment Evidencecited by 3
  14. 2025Detecting LLM Fact-conflicting Hallucinations Enhanced by Temporal-logic-based Reasoningcited by 2
  15. 2025Finetune-RAG: Fine-Tuning Language Models to Resist Hallucination in Retrieval-Augmented Generationcited by 2
  16. 2024Unveiling and Consulting Core Experts in Retrieval-Augmented MoE-based LLMscited by 1
  17. 2026Beyond In-Domain Detection: SpikeScore for Cross-Domain Hallucination Detectioncited by 1
  18. 2026OpenHalDet: A Unified Benchmark for Hallucination Detection across Diverse Generation Scenarioscited by 1
  19. 2025Valuable Hallucinations: Realizable Non-realistic Propositionscited by 0
  20. 2025Won: Establishing Best Practices for Korean Financial NLPcited by 0
  21. 2025Proof-Carrying Numbers (PCN): A Protocol for Trustworthy Numeric Answers from LLMs via Claim Verificationcited by 0
  22. 2025Personalized Chain-of-Thought Summarization of Financial News for Investor Decision Supportcited by 0
  23. 2026HTDC: Hesitation-Triggered Differential Calibration for Mitigating Hallucination in Large Vision-Language Modelscited by 0
  24. 2026You Snooze, You Lose: Automatic Safety Alignment Restoration through Neural Weight Translationcited by 0
  25. 2026FinVerBench: Benchmark Validity and Calibration in Large Language Model Financial Statement Verificationcited by 0
  26. 2026Towards Lightweight Reliability: Using Soft Prompts for Hallucination Mitigation in Large Language Modelscited by 0
  27. 2026BALTO: Balanced Token-Level Policy Optimization for Hallucination Mitigationcited by 0
  28. 2026Hallucination Detection in Large Language Models Using Diversion Decodingcited by 0
  29. 2026FaVOR: LLM-Based Agentic Framework for Factor Mining via Empirical Validationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Empowering Small Language Models with Factual Hallucination-Aware Reasoning for Financial Classification
  2. 2026K-FinHallu: A Hallucination Detection Benchmark for Multi-Turn RAG in Korean Finance
  3. 2025FRED: Financial Retrieval-Enhanced Detection and Editing of Hallucinations in Language Models
  4. 2023Towards reducing hallucination in extracting information from financial reports using Large Language Models
  5. 2023Beyond Classification: Financial Reasoning in State-of-the-Art Language Models
  6. 2025Dissecting the Ledger: Locating and Suppressing "Liar Circuits" in Financial Large Language Models
  7. 2025Reasoning or Overthinking: Evaluating Large Language Models on Financial Sentiment Analysis
  8. 2025Measuring What LLMs Think They Do: SHAP Faithfulness and Deployability on Financial Tabular Classification