mlsanityData to 7 Oct 2026

Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models

arXiv 2310.04027 ↗First posted 6 Oct 2023cs.CL q-fin.ST q-fin.TRcited by 67

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

Abstract (arXiv)

Financial sentiment analysis is critical for valuation and investment decision-making. Traditional NLP models, however, are limited by their parameter size and the scope of their training datasets, which hampers their generalization capabilities and effectiveness in this field. Recently, Large Language Models (LLMs) pre-trained on extensive corpora have demonstrated superior performance across various NLP tasks due to their commendable zero-shot abilities. Yet, directly applying LLMs to financial sentiment analysis presents challenges: The discrepancy between the pre-training objective of LLMs and predicting the sentiment label can compromise their predictive performance. Furthermore, the succinct nature of financial news, often devoid of sufficient context, can significantly diminish the reliability of LLMs' sentiment analysis. To address these challenges, we introduce a retrieval-augmented LLMs framework for financial sentiment analysis. This framework includes an instruction-tuned LLMs module, which ensures LLMs behave as predictors of sentiment labels, and a retrieval-augmentation module which retrieves additional context from reliable external sources. Benchmarked against traditional models and LLMs like ChatGPT and LLaMA, our approach achieves 15% to 48% performance gain in accuracy and F1 score.

Key facts. Cited by 67 papers in the map (data through 7 Oct 2026). Placed in Legal and financial RAG, one of 15 directions in Retrieval-augmented generation. Builds on 22 of its 36 references that are in the map. First posted on arXiv 6 Oct 2023.

Builds on

22 of 36 references.

References · 22

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Training language models to follow instructions with human feedbackcited by 15,712
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  5. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  6. 2021Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsitycited by 3,014
  7. 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,898
  8. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,674
  9. 2017Decoupled Weight Decay Regularizationcited by 1,722
  10. 2021Multitask Prompted Training Enables Zero-Shot Task Generalizationcited by 1,462
  11. 2022LaMDA: Language Models for Dialog Applicationscited by 1,218
  12. 2023BloombergGPT: A Large Language Model for Financecited by 923
10 more
  1. 2022GLM-130B: An Open Bilingual Pre-trained Modelcited by 837
  2. 2019FinBERT: Financial Sentiment Analysis with Pre-trained Language Modelscited by 378
  3. 2013Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Textscited by 310
  4. 2023FinGPT: Open-Source Financial Large Language Modelscited by 244
  5. 2020Generation-Augmented Retrieval for Open-domain Question Answeringcited by 186
  6. 2020FinBERT: A Pretrained Language Model for Financial Communicationscited by 161
  7. 2021Retrieval Augmented Code Generation and Summarizationcited by 148
  8. 2020Retrieval-Augmented Generation for Code Summarization via Hybrid GNNcited by 89
  9. 2023Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Modelscited by 58
  10. 2023Large Language Model Instruction Following: A Survey of Progresses and Challengescited by 31

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 32; 2025: 24; 2026: 8.

67 papers.

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  1. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Modelscited by 450
  2. 2024Revolutionizing Finance with LLMs: An Overview of Applications and Insightscited by 88
  3. 2024A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challengescited by 86
  4. 2024BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Modelscited by 84
  5. 2024Follow My Instruction and Spill the Beans: Scalable Data Extraction from Retrieval-Augmented Generation Systemscited by 76
  6. 2024EmoLLMs: A Series of Emotional Large Language Models and Annotation Tools for Comprehensive Affective Analysiscited by 62
  7. 2024RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generationcited by 59
  8. 2025RAG vs. GraphRAG: A Systematic Evaluation and Key Insightscited by 51
  9. 2024Designing Heterogeneous LLM Agents for Financial Sentiment Analysiscited by 48
  10. 2024Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologiescited by 44
  11. 2023Deficiency of Large Language Models in Finance: An Empirical Examination of Hallucinationcited by 41
  12. 2024FinRobot: An Open-Source AI Agent Platform for Financial Applications using Large Language Modelscited by 39
38 more
  1. 2024TelecomGPT: A Framework to Build Telecom-Specfic Large Language Modelscited by 38
  2. 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generationcited by 34
  3. 2024Affective Computing in the Era of Large Language Models: A Survey from the NLP Perspectivecited by 24
  4. 2024Harnessing Earnings Reports for Stock Predictions: A QLoRA-Enhanced LLM Approachcited by 24
  5. 2024Embedding-based classifiers can detect prompt injection attackscited by 24
  6. 2024Reassessing Layer Pruning in LLMs: New Insights and Methodscited by 23
  7. 2023Emotion Detection for Misinformation: A Reviewcited by 20
  8. 2025FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generationcited by 17
  9. 2025CPA-RAG:Covert Poisoning Attacks on Retrieval-Augmented Generation in Large Language Modelscited by 17
  10. 2025MarketSenseAI 2.0: Enhancing Stock Analysis through LLM Agentscited by 16
  11. 2024CTSyn: A Foundation Model for Cross Tabular Data Generationcited by 13
  12. 2024AuditLLM: A Tool for Auditing Large Language Models Using Multiprobe Approachcited by 11
  13. 2025FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasetscited by 11
  14. 2024AI in Investment Analysis: LLMs for Equity Stock Ratingscited by 10
  15. 2025Large Language Models for Subjective Language Understanding: A Surveycited by 9
  16. 2025A Large Language Model-Empowered Agent for Reliable and Robust Structural Analysiscited by 8
  17. 2025Retrieval-Augmented Generation in Industry: An Interview Study on Use Cases, Requirements, Challenges, and Evaluationcited by 8
  18. 2026Query-Aware Flow Diffusion for Graph-Based RAG with Retrieval Guaranteescited by 7
  19. 2024Large Language Model Enhanced Knowledge Representation Learning: A Surveycited by 6
  20. 2024FinVision: A Multi-Agent Framework for Stock Market Predictioncited by 6
  21. 2024LLM-SEM: A Sentiment-Based Student Engagement Metric Using LLMS for E-Learning Platformscited by 6
  22. 2024SoK: Prompt Hacking of Large Language Modelscited by 5
  23. 2025Are Large Language Models Good In-context Learners for Financial Sentiment Analysis?cited by 5
  24. 2025Interpretable LLMs for Credit Risk: A Systematic Review and Taxonomycited by 5
  25. 2026Confundo: Learning to Generate Robust Poison for Practical RAG Systemscited by 5
  26. 2024RiskLabs: Predicting Financial Risk Using Large Language Model based on Multimodal and Multi-Sources Datacited by 4
  27. 2025Bridging Language Models and Financial Analysiscited by 4
  28. 2025REIS: A High-Performance and Energy-Efficient Retrieval System with In-Storage Processingcited by 4
  29. 2024Large Language Model in Financial Regulatory Interpretationcited by 3
  30. 2024ConspEmoLLM: Conspiracy Theory Detection Using an Emotion-Based Large Language Modelcited by 2
  31. 2024Schrodinger's Memory: Large Language Modelscited by 2
  32. 2024Optimizing Performance: How Compact Models Match or Exceed GPT's Classification Capabilities through Fine-Tuningcited by 2
  33. 2024AMPLE: Emotion-Aware Multimodal Fusion Prompt Learning for Fake News Detectioncited by 2
  34. 2025LLMs for Supply Chain Managementcited by 2
  35. 2025Evaluating Large Language Models (LLMs) in Financial NLP: A Comparative Study on Financial Report Analysiscited by 2
  36. 2026Core-based Hierarchies for Efficient GraphRAGcited by 2
  37. 2024Customized FinGPT Search Agents Using Foundation Modelscited by 1
  38. 2024Regularized Multi-LLMs Collaboration for Enhanced Score-based Causal Discoverycited by 1

50 most cited of 67 All 67 (JSON)

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  2. 2022What do LLMs Know about Financial Markets? A Case Study on Reddit Market Sentiment Analysis
  3. 2025FinBERT2: A Specialized Bidirectional Encoder for Bridging the Gap in Finance-Specific Deployment of Large Language Models
  4. 2025Reasoning or Overthinking: Evaluating Large Language Models on Financial Sentiment Analysis
  5. 2024Multi-Reranker: Maximizing performance of retrieval-augmented generation in the FinanceRAG challenge
  6. 2024Financial Sentiment Analysis: Leveraging Actual and Synthetic Data for Supervised Fine-tuning
  7. 2024FinBen: A Holistic Financial Benchmark for Large Language Models
  8. 2025Reliable Decision Support with LLMs: A Framework for Evaluating Consistency in Binary Text Classification Applications