Revolutionizing Finance with LLMs: An Overview of Applications and
  Insights
Huaqin Zhao Zhengliang Liu Zihao Wu Yiwei Li Tianze Yang Peng Shu Shaochen Xu Haixing Dai Lin Zhao Gengchen Mai Ninghao Liu Tianming Liu
In recent years, Large Language Models (LLMs) like ChatGPT have seen
considerable advancements and have been applied in diverse fields. Built on the
Transformer architecture, these models are trained on extensive datasets,
enabling them to understand and generate human language effectively. In the
financial domain, the deployment of LLMs is gaining momentum. These models are
being utilized for automating financial report generation, forecasting market
trends, analyzing investor sentiment, and offering personalized financial
advice. Leveraging their natural language processing capabilities, LLMs can
distill key insights from vast financial data, aiding institutions in making
informed investment choices and enhancing both operational efficiency and
customer satisfaction. In this study, we provide a comprehensive overview of
the emerging integration of LLMs into various financial tasks. Additionally, we
conducted holistic tests on multiple financial tasks through the combination of
natural language instructions. Our findings show that GPT-4 effectively follow
prompt instructions across various financial tasks. This survey and evaluation
of LLMs in the financial domain aim to deepen the understanding of LLMs'
current role in finance for both financial practitioners and LLM researchers,
identify new research and application prospects, and highlight how these
technologies can be leveraged to solve practical challenges in the finance
industry.