RAG-IT: Retrieval-Augmented Instruction Tuning for Automated Financial Analysis
This work addresses the problem of time-consuming and expertise-heavy financial analysis for professionals, representing an incremental improvement through domain-specific adaptation of existing methods.
The paper tackled automating earnings report analysis for financial decision-making by introducing RAG-IT, a framework that integrates retrieval augmentation with instruction tuning for LLMs, achieving performance comparable to GPT-3.5 on financial report generation tasks.
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.