# Fine-tuning and Utilization Methods of Domain-specific LLMs

**Authors:** Cheonsu Jeong  
**arXiv:** [2401.02981](https://arxiv.org/abs/2401.02981)  
**Date:** 2024-01-01  
**Summary page:** https://www.summarizepaper.com/en/paper/2401.02981v1/

## Key points

- Limited research on the application of pre-trained Large Language Models (LLMs) in specific domains, particularly in finance
- Various approaches for fine-tuning and leveraging domain-specific LLMs
- Trends in LLMs, foundational models, and methods for domain-specific pre-training
- Considerations for LLM fine-tuning in finance: dataset selection, preprocessing techniques, and model choice
- Importance of constructing domain-specific vocabularies to improve performance with financial data
- Emphasis on security and regulatory compliance when working with financial data
- Procedure outlined for generating domain-specific LLMs in finance
- Potential use cases for LLMs in finance: stock price prediction, sentiment analysis of financial news, automated document processing, research tasks, information extraction, and customer service enhancement
- Identification of limitations and proposed directions for improvement of LLMs in the financial domain

## Summary

This study by Cheonsu Jeong examines the methods of fine-tuning and utilizing domain-specific Large Language Models (LLMs) in the financial sector. Despite the popularity of pre-trained LLMs, there is limited research on their application in specific domains. The research explores various approaches for fine-tuning and leveraging domain-specific LLMs, highlighting trends in LLMs, foundational models, and methods for domain-specific pre-training. It also addresses important considerations for LLM fine-tuning in finance such as dataset selection, preprocessing techniques, and model choice. Additionally, the study emphasizes the unique characteristics of financial data and suggests constructing domain-specific vocabularies to improve performance. Furthermore, it stresses the importance of security and regulatory compliance when working with financial data. In terms of practical implementation, a procedure for generating domain-specific LLMs in finance is outlined. The study provides insights into potential use cases for LLMs in finance including stock price prediction, sentiment analysis of financial news, automated document processing, research tasks, information extraction, and customer service enhancement. It thoroughly explores the potential of LLMs in the financial domain while identifying limitations and proposing directions for improvement. Overall  have great potential to advance language processing capabilities within the . This study offers valuable insights for future research in natural language processing technology within  industries and suggests proactive utilization of LLMs in financial services across industries.