RAG-IT: Retrieval-Augmented Instruction Tuning for Automated Financial Analysis – A Case Study for the Semiconductor Sector

ArXiv ID: 2412.08179 “View on arXiv”

Authors: Unknown

Abstract

Financial analysis relies heavily on the interpretation of earnings reports to assess company performance and guide decision-making. Traditional methods for generating such analyzes require significant financial expertise and are often time-consuming. With the rapid advancement of Large Language Models (LLMs), domain-specific adaptations have emerged for financial tasks such as sentiment analysis and entity recognition. This paper introduces RAG-IT (Retrieval-Augmented Instruction Tuning), a novel framework designed to automate the generation of earnings report analysis through an LLM fine-tuned specifically for the financial domain. Our approach integrates retrieval augmentation with instruction-based fine-tuning to enhance factual accuracy, contextual relevance, and domain adaptability. We construct a sector-specific financial instruction dataset derived from semiconductor industry documents to guide the LLM adaptation to specialized financial reasoning. Using NVIDIA, AMD, and Broadcom as representative companies, our case study demonstrates that RAG-IT substantially improves a general-purpose open-source LLM and achieves performance comparable to commercial systems like GPT-3.5 on financial report generation tasks. This research highlights the potential of retrieval-augmented instruction tuning to streamline and elevate financial analysis automation, advancing the broader field of intelligent financial reporting.

Keywords: Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Instruction Tuning, Financial Sentiment Analysis, Earnings Reports, Equities

Complexity vs Empirical Score

• Math Complexity: 2.0/10
• Empirical Rigor: 8.0/10
• Quadrant: Street Traders
• Why: The paper uses minimal advanced mathematics, focusing on instruction tuning and retrieval augmented generation methodologies without heavy formulas or derivations. It demonstrates strong empirical rigor by constructing a sector-specific dataset, fine-tuning open-source LLMs, and comparing performance against commercial systems like GPT-3.5 on financial report generation tasks.

  flowchart TD
    A["Research Goal: Automate Financial Analysis<br>for Semiconductor Earnings Reports"] --> B
    subgraph B ["Methodology: RAG-IT Framework"]
        direction TB
        B1["Data Collection:<br>Sector-Specific Documents"] --> B2["Instruction Tuning:<br>Fine-tune LLM on Financial Dataset"] --> B3["Retrieval Augmentation:<br>Integrate Knowledge Base during Inference"]
    end
    B --> C["Computational Process:<br>Generate Analysis for NVIDIA, AMD, Broadcom"]
    C --> D["Key Findings:<br>1. Substantially improves open-source LLMs<br>2. Matches commercial systems (GPT-3.5)<br>3. Enhances factual accuracy & relevance"]