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.

近期大规模语言模型(LLM)的发展在金融领域开创了人工智能应用的新可能性。本文提供了一份实用的调查报告，关注LLM在金融任务中的两个关键方面：现有解决方案和采用指南。首先，我们回顾了目前在金融领域中采用LLM的方法，包括通过零样本或少样本学习利用预训练模型，对领域特定数据进行微调，以及从头开始训练自定义LLM。我们总结了关键模型并评估了它们在金融自然语言处理任务上的性能改进。其次，我们提出了一个决策框架，以帮助金融业专业人员根据数据、计算和性能需求选择合适的LLM解决方案。该框架提供了从轻量级实验到大规模投资自定义LLM的路径。最后，我们讨论了在金融应用中利用LLM所面临的限制和挑战。总的来说，该调查旨在综述最新技术并为负责任地应用LLM推动金融人工智能提供路线图。

金融中的大型语言模型：综述