Recent advances in large language models (LLMs) have unlocked novel
opportunities for machine learning applications in the financial domain. These
models have demonstrated remarkable capabilities in understanding context,
processing vast amounts of data, and generating human-preferred contents. In
this survey, we explore the application of LLMs on various financial tasks,
focusing on their potential to transform traditional practices and drive
innovation. We provide a discussion of the progress and advantages of LLMs in
financial contexts, analyzing their advanced technologies as well as
prospective capabilities in contextual understanding, transfer learning
flexibility, complex emotion detection, etc. We then highlight this survey for
categorizing the existing literature into key application areas, including
linguistic tasks, sentiment analysis, financial time series, financial
reasoning, agent-based modeling, and other applications. For each application
area, we delve into specific methodologies, such as textual analysis,
knowledge-based analysis, forecasting, data augmentation, planning, decision
support, and simulations. Furthermore, a comprehensive collection of datasets,
model assets, and useful codes associated with mainstream applications are
presented as resources for the researchers and practitioners. Finally, we
outline the challenges and opportunities for future research, particularly
emphasizing a number of distinctive aspects in this field. We hope our work can
help facilitate the adoption and further development of LLMs in the financial
sector.

使用大型语言模型（LLMs）在金融领域中的机器学习应用的最新进展，探讨了 LLMs 在各种金融任务上的应用，重点在于它们改变传统做法和推动创新的潜力，提供了进展和优势的讨论，包括上下文理解、迁移学习的灵活性和复杂情感检测等高级技术，以及将现有文献分类为主要应用领域，包括语言任务、情感分析、金融时间序列、金融推理、基于代理的建模和其他应用，详细介绍了每个应用领域的具体方法论，如文本分析、基于知识的分析、预测、数据增强、规划、决策支持和模拟。此外，还提供了与主流应用相关的数据集、模型资源和有用代码等资源，作为研究人员和实践者的参考。最后，概述了未来研究中的挑战和机遇，特别强调了该领域的几个独特方面。希望我们的工作能促进 LLMs 在金融领域的采用和进一步发展。