Revolutionizing Finance with LLMs: An Overview of Applications and Insights
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arXiv
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| Format: | Preprint |
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2024
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| author | Zhao, Huaqin Liu, Zhengliang Wu, Zihao Li, Yiwei Yang, Tianze Shu, Peng Xu, Shaochen Dai, Haixing Zhao, Lin Jiang, Hanqi Pan, Yi Chen, Junhao Zhou, Yifan Zhang, Zeyu Sun, Ruitong Mai, Gengchen Liu, Ninghao Liu, Tianming |
| author_facet | Zhao, Huaqin Liu, Zhengliang Wu, Zihao Li, Yiwei Yang, Tianze Shu, Peng Xu, Shaochen Dai, Haixing Zhao, Lin Jiang, Hanqi Pan, Yi Chen, Junhao Zhou, Yifan Zhang, Zeyu Sun, Ruitong Mai, Gengchen Liu, Ninghao Liu, Tianming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_11641 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Revolutionizing Finance with LLMs: An Overview of Applications and Insights Zhao, Huaqin Liu, Zhengliang Wu, Zihao Li, Yiwei Yang, Tianze Shu, Peng Xu, Shaochen Dai, Haixing Zhao, Lin Jiang, Hanqi Pan, Yi Chen, Junhao Zhou, Yifan Zhang, Zeyu Sun, Ruitong Mai, Gengchen Liu, Ninghao Liu, Tianming Computation and Language 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. |
| title | Revolutionizing Finance with LLMs: An Overview of Applications and Insights |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2401.11641 |