[Paper Review] Fine-tuning and Utilization Methods of Domain-specific LLMs

C. Jeong|arXiv (Cornell University)|Jan 1, 2024

Stock Market Forecasting Methods10 references23 citations

TL;DR

The paper surveys fine-tuning and utilization strategies for domain-specific LLMs, with a focus on the financial sector, including data, vocabulary, security, and practical deployment considerations.

ABSTRACT

Recent releases of pre-trained Large Language Models (LLMs) have gained considerable traction, yet research on fine-tuning and employing domain-specific LLMs remains scarce. This study investigates approaches for fine-tuning and leveraging domain-specific LLMs, highlighting trends in LLMs, foundational models, and methods for domain-specific pre-training. Focusing on the financial sector, it details dataset selection, preprocessing, model choice, and considerations crucial for LLM fine-tuning in finance. Addressing the unique characteristics of financial data, the study explores the construction of domain-specific vocabularies and considerations for security and regulatory compliance. In the practical application of LLM fine-tuning, the study outlines the procedure and implementation for generating domain-specific LLMs in finance. Various financial cases, including stock price prediction, sentiment analysis of financial news, automated document processing, research, information extraction, and customer service enhancement, are exemplified. The study explores the potential of LLMs in the financial domain, identifies limitations, and proposes directions for improvement, contributing valuable insights for future research. Ultimately, it advances natural language processing technology in business, suggesting proactive LLM utilization in financial services across industries.

Motivation & Objective

• Motivate the study by noting the scarcity of domain-specific LLM fine-tuning research.
• Survey trends in LLMs, foundational models, and domain-specific pre-training.
• Detail dataset selection, preprocessing, and model choice for finance-focused fine-tuning.
• Identify domain-specific vocabulary construction and regulatory/security considerations for financial data.
• Outline a practical procedure for generating domain-specific LLMs in finance and discuss applications and limitations.

Proposed method

• Review existing approaches to fine-tuning domain-specific LLMs.
• Analyze dataset selection and preprocessing strategies for finance-oriented models.
• Discuss model choices suitable for domain-specific tasks in finance.
• Explain construction of domain-specific vocabularies and security/regulatory considerations.
• Provide a practical procedure for generating domain-specific LLMs in finance.

Experimental results

Research questions

• RQ1 What approaches are effective for fine-tuning LLMs to domain-specific tasks, particularly in finance?
• RQ2 How should financial data be prepared and used to train or fine-tune domain-specific LLMs?
• RQ3 How can domain-specific vocabularies be constructed and integrated into LLMs for finance?
• RQ4 What security and regulatory considerations are essential when deploying finance-focused LLMs?
• RQ5 What practical procedure can be followed to generate and deploy domain-specific LLMs in the financial sector?

Key findings

• The study highlights potential benefits of domain-specific LLMs in finance across applications like stock price prediction, sentiment analysis, automated document processing, information extraction, research, and customer service.
• It discusses limitations and proposes directions for improvement in domain-specific LLM fine-tuning and utilization.
• It contributes practical insights for implementing finance-focused LLMs and emphasizes proactive utilization in financial services.
• The work outlines a practical procedure for generating domain-specific LLMs in the finance domain.