The Development of Large Language Models in the Financial Field

Abstract

With the rapid development of natural language processing (NLP) and machine learning technology, applying large language models (LLMs) in the financial field shows a significant growth trend. This paper systematically reviews the development status, main applications, challenges, and future development direction of LLMs in the financial field. Financial Language models (FinLLMs) have been successfully applied to many scenarios, such as sentiment analysis, automated trading, risk assessment, etc., through deep learning architectures such as BERT, Llama, and domain data fine-tuning. However, issues such as data privacy, model interpretability, and ethical governance still pose constraints to their widespread application. Future research should focus on improving model performance, addressing bias issues, strengthening privacy protection, and establishing a sound regulatory framework to ensure the healthy development of LLMs in the financial sector.