This survey paper provides a comprehensive examination of large language model (LLM) applications in the financial domain, highlighting both current progress and future prospects. The authors systematically review how LLMs are transforming traditional financial practices through their advanced capabilities in contextual understanding, transfer learning flexibility, and complex emotion detection. The paper categorizes existing literature into six key application areas: linguistic tasks, sentiment analysis, financial time series, financial reasoning, agent-based modeling, and other applications, providing detailed analysis of methodologies within each category.

The survey delves into specific techniques including textual analysis, knowledge-based analysis, forecasting, data augmentation, planning, decision support, and simulations, demonstrating how LLMs are being adapted for financial contexts. A significant contribution is the compilation of datasets, model assets, and code resources that serve as practical tools for researchers and practitioners. The paper concludes by outlining distinctive challenges in the field, such as data privacy concerns, model interpretability requirements, and regulatory compliance issues, while identifying opportunities for future research that could accelerate LLM adoption in the financial sector.