# A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges

**Authors:** Yuqi Nie, Yaxuan Kong, Xiaowen Dong, John M. Mulvey, H. Vincent Poor, Qingsong Wen, Stefan Zohren  
**arXiv:** [2406.11903](https://arxiv.org/abs/2406.11903)  
**Date:** 2024-06-15  
**Summary page:** https://www.summarizepaper.com/en/paper/2406.11903v1/

## Key points

- Recent advances in large language models (LLMs) have revolutionized machine learning applications within the financial domain.
- A team of esteemed researchers led by Yuqi Nie, Yaxuan Kong, Xiaowen Dong, John M. Mulvey, H. Vincent Poor, Qingsong Wen, and Stefan Zohren conducted a comprehensive survey on utilizing LLMs for financial applications.
- LLMs demonstrate remarkable capabilities in understanding context and processing vast amounts of data to generate human-preferred content.
- The study explores various financial tasks and highlights how LLMs can transform traditional practices and drive innovation within the industry.
- Existing literature is categorized into key application areas including linguistic tasks, sentiment analysis, financial time series analysis, financial reasoning processes, agent-based modeling techniques among others.
- Advanced technologies such as contextual understanding, transfer learning flexibility, and complex emotion detection showcase the versatility of LLMs in different contexts.
- Specific methodologies like textual analysis, knowledge-based analysis forecasting methods data augmentation strategies planning approaches decision support systems simulations tools are discussed along with a collection of datasets model assets useful codes associated with mainstream applications as valuable resources for researchers and practitioners.
- Challenges opportunities for future research are outlined to facilitate adoption further development of LLMs in enhancing financial sector operations.

## Summary

Recent advances in large language models (LLMs) have revolutionized the landscape of machine learning applications within the financial domain. Led by a team of esteemed researchers including Yuqi Nie, Yaxuan Kong, Xiaowen Dong, John M. Mulvey, H. Vincent Poor, Qingsong Wen, and Stefan Zohren, their comprehensive survey delves deep into the progress and prospects associated with utilizing LLMs for financial applications. The study highlights the remarkable capabilities demonstrated by LLMs in understanding context and processing vast amounts of data to generate human-preferred content. By exploring various financial tasks, the researchers shed light on how LLMs have the potential to transform traditional practices and drive innovation within the industry. The survey categorizes existing literature into key application areas including linguistic tasks, sentiment analysis, financial time series analysis, financial reasoning processes, agent-based modeling techniques among others. It analyzes advanced technologies such as contextual understanding, transfer learning flexibility and complex emotion detection to showcase the versatility of LLMs in different contexts. Delving into specific methodologies like textual analysis, knowledge-based analysis forecasting methods data augmentation strategies planning approaches decision support systems simulations tools; the researchers provide a comprehensive collection of datasets model assets useful codes associated with mainstream applications as valuable resources for both researchers and practitioners in the field. Furthermore outlining challenges opportunities for future research particularly emphasizing distinctive aspects within this domain; this work aims to facilitate adoption further development of LLMs in enhancing financial sector operations. With a focus on driving innovation and transforming traditional practices using cutting-edge technology this survey serves as a valuable resource for those looking to leverage LLMs for financial applications.