RAG-IT: Retrieval-Augmented Instruction Tuning for Automated Financial Analysis

• URL: http://arxiv.org/abs/2412.08179v2
• Date: Wed, 05 Nov 2025 13:53:51 GMT
• Title: RAG-IT: Retrieval-Augmented Instruction Tuning for Automated Financial Analysis
• Authors: Van-Duc Le, Hai-Thien To,
• Abstract summary: RAG-IT (Retrieval-Augmented Instruction Tuning) is a novel framework designed to automate the generation of earnings report analyses.<n>Our approach integrates retrieval augmentation with instruction-based fine-tuning to enhance factual accuracy, contextual relevance, and domain adaptability.
• Score: 1.2891210250935148
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Financial analysis relies heavily on the interpretation of earnings reports to assess company performance and guide decision-making. Traditional methods for generating such analyses demand 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 analyses 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 comprehensive financial instruction dataset derived from extensive financial documents and earnings reports to guide the LLM's adaptation to specialized financial reasoning. Experimental results demonstrate that RAG-IT outperforms general-purpose open-source models 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.

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