Large Language Models in Finance: A Survey. (arXiv:2311.10723v1 [q-fin.GN])

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.