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取得了显著进展并被广泛应用于多个领域。这类模型基于Transformer架构，通过海量数据集训练，能够有效理解和生成人类语言。在金融领域，大语言模型的部署正日益加速，目前已应用于自动化财务报告生成、市场趋势预测、投资者情绪分析及个性化金融建议等场景。凭借其自然语言处理能力，这些模型能从海量金融数据中提炼关键洞见，助力机构做出明智投资决策，同时提升运营效率与客户满意度。本研究全面梳理了大语言模型在多元金融任务中的新兴融合趋势，并基于自然语言指令组合对多类金融任务进行了系统性测试。研究结果表明，GPT-4能有效遵循各类金融任务的提示指令。本综述与评估旨在深化金融从业者与大语言模型研究者对其当前金融应用的理解，识别新的研究与应用前景，并揭示如何利用这些技术解决金融行业的现实挑战。