FinGPT: Open-Source Financial Large Language Models
Hongyang Yang Xiao-Yang Liu Christina Dan Wang
Large language models (LLMs) have shown the potential of revolutionizing
natural language processing tasks in diverse domains, sparking great interest
in finance. Accessing high-quality financial data is the first challenge for
financial LLMs (FinLLMs). While proprietary models like BloombergGPT have taken
advantage of their unique data accumulation, such privileged access calls for
an open-source alternative to democratize Internet-scale financial data.
  In this paper, we present an open-source large language model, FinGPT, for
the finance sector. Unlike proprietary models, FinGPT takes a data-centric
approach, providing researchers and practitioners with accessible and
transparent resources to develop their FinLLMs. We highlight the importance of
an automatic data curation pipeline and the lightweight low-rank adaptation
technique in building FinGPT. Furthermore, we showcase several potential
applications as stepping stones for users, such as robo-advising, algorithmic
trading, and low-code development. Through collaborative efforts within the
open-source AI4Finance community, FinGPT aims to stimulate innovation,
democratize FinLLMs, and unlock new opportunities in open finance. Two
associated code repos are \url{https://github.com/AI4Finance-Foundation/FinGPT}
and \url{https://github.com/AI4Finance-Foundation/FinNLP}