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.

翻译：近年来，以ChatGPT为代表的大语言模型（LLMs）取得了显著进展，并已应用于众多领域。这些模型基于Transformer架构构建，通过在海量数据集上进行训练，能够有效理解并生成人类语言。在金融领域，大语言模型的部署正日益受到关注。这些模型被用于自动化生成财务报告、预测市场趋势、分析投资者情绪以及提供个性化金融建议。借助其自然语言处理能力，大语言模型能够从海量金融数据中提炼关键信息，帮助机构做出明智的投资决策，同时提升运营效率与客户满意度。本研究全面综述了大语言模型在各类金融任务中的新兴融合应用。此外，我们通过结合自然语言指令对多项金融任务进行了系统性测试。研究结果表明，GPT-4能够有效遵循不同金融任务中的指令提示。本次针对金融领域大语言模型的综述与评估，旨在加深金融从业者与LLM研究者对其当前金融应用的理解，发掘新的研究与应用前景，并强调如何利用这些技术解决金融行业面临的实际挑战。