  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.
