Large Language Models in Finance: A Survey
Yinheng Li Shaofei Wang Han Ding Hang Chen
Recent advances in large language models (LLMs) have opened new possibilities
for artificial intelligence applications in finance. In this paper, we provide
a practical survey focused on two key aspects of utilizing LLMs for financial
tasks: existing solutions and guidance for adoption.
  First, we review current approaches employing LLMs in finance, including
leveraging pretrained models via zero-shot or few-shot learning, fine-tuning on
domain-specific data, and training custom LLMs from scratch. We summarize key
models and evaluate their performance improvements on financial natural
language processing tasks.
  Second, we propose a decision framework to guide financial professionals in
selecting the appropriate LLM solution based on their use case constraints
around data, compute, and performance needs. The framework provides a pathway
from lightweight experimentation to heavy investment in customized LLMs.
  Lastly, we discuss limitations and challenges around leveraging LLMs in
financial applications. Overall, this survey aims to synthesize the
state-of-the-art and provide a roadmap for responsibly applying LLMs to advance
financial AI.