Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models

ArXiv ID: 2310.04027 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: Financial Sentiment Analysis, Large Language Models (LLMs), Retrieval-Augmented Generation, Natural Language Processing, Investment Decision-Making, Equities

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 7.5/10
• Quadrant: Street Traders
• Why: The paper applies established NLP techniques (RAG, instruction tuning) with minimal novel mathematical derivations, focusing on engineering a practical framework, but is heavily backed by benchmark results (15-48% gains) and comparisons against established models like ChatGPT and LLaMA, demonstrating strong empirical backing.

  flowchart TD
    A["Research Goal:<br>Enhance Financial Sentiment Analysis"] --> B{"Core Problem: Traditional NLP &<br>Direct LLM Application Limitations"}
    B --> C["Proposed Method:<br>Retrieval-Augmented LLM Framework"]
    
    subgraph C_Method ["Framework Components"]
        C1["Instruction-Tuned LLM Module<br>Acts as Label Predictor"]
        C2["Retrieval-Augmentation Module<br>Supplements Context from External Sources"]
    end

    C --> C_Method
    
    C_Method --> D["Input Data:<br>Financial News & Market Data"]
    D --> E["Computational Process:<br>Retrieval + LLM Inference"]
    E --> F["Output: Accurate<br>Sentiment Labels"]
    
    F --> G["Key Outcomes"]
    subgraph G_Results ["Benchmark Results"]
        G1["15% to 48% Gain in Accuracy & F1"]
        G2["Outperforms Baselines<br>(Traditional NLP, ChatGPT, LLaMA)"]
    end
    
    G --> G_Results