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

Paper: arXiv 2412.08179

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.

Complexity vs Empirical Score

• Math Complexity: 2.0/10
• Empirical Rigor: 8.0/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: 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.

Research Flowchart

  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"]

Cited References (18)

1. Retrieval-augmented generation for knowledge-intensive nlp tasks (2020)
2. Qlora: Efficient finetuning of quantized llms (2023)
3. Llama 2: Open foundation and fine-tuned chat models (2023) — link
4. Alpaca: A strong, replicable instruction-following model (2023)
5. Visual instruction tuning (2023)
6. BloombergGPT: A large language model for finance, 2023 (2024)
7. FinGPT: Open-Source Financial Large Language Models — → 2306.06031
8. Lora: Low-rank adaptation of large language models. (2022)

2 of 18 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.