The article "Revolutionizing Finance with LLMs: An Overview of Applications and Insights" by Huaqin Zhao et al. explores the growing integration of Large Language Models (LLMs) into various financial tasks. LLMs, built on the Transformer architecture and trained on extensive datasets, are increasingly being used in the financial domain for automating financial report generation, forecasting market trends, analyzing investor sentiment, and offering personalized financial advice. The authors provide a comprehensive overview of the emerging applications of LLMs in financial engineering, forecasting, risk management, and real-time question answering. They highlight the challenges and solutions in applying LLMs to financial data, emphasizing the need for specialized training and expert systems to ensure accuracy and reliability. The study also evaluates the effectiveness of GPT-4 in these tasks, demonstrating its ability to follow prompt instructions across various financial applications. The article aims to deepen the understanding of LLMs' role in finance, identify new research and application prospects, and highlight how these technologies can solve practical challenges in the financial industry.The article "Revolutionizing Finance with LLMs: An Overview of Applications and Insights" by Huaqin Zhao et al. explores the growing integration of Large Language Models (LLMs) into various financial tasks. LLMs, built on the Transformer architecture and trained on extensive datasets, are increasingly being used in the financial domain for automating financial report generation, forecasting market trends, analyzing investor sentiment, and offering personalized financial advice. The authors provide a comprehensive overview of the emerging applications of LLMs in financial engineering, forecasting, risk management, and real-time question answering. They highlight the challenges and solutions in applying LLMs to financial data, emphasizing the need for specialized training and expert systems to ensure accuracy and reliability. The study also evaluates the effectiveness of GPT-4 in these tasks, demonstrating its ability to follow prompt instructions across various financial applications. The article aims to deepen the understanding of LLMs' role in finance, identify new research and application prospects, and highlight how these technologies can solve practical challenges in the financial industry.