FinGPT: Open-Source Financial Large Language Models

By Hongyang Yang, Xiao-Yang Liu, Chris Wang. Social Science Research Network, 2023

434 citations recorded by the source corpus.

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 https://github.com/AI4Finance-Foundation/FinGPT and https://github.com/AI4Finance-Foundation/FinNLP

View Paper: https://www.semanticscholar.org/paper/5dea206e2a36e672f197252bdd27d156d058f48c

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Citations in the corpus

Recommender Systems in the Era of Large Language Models (LLMs) (2023, unknown): “Due to the huge gap between language generation tasks ( i.e. , the pre-training objectives of LLMs) and down-stream recommendation tasks, these conventional prompting methods have only shown limited applications in specific recommendation tasks that have similar nature to language generation tasks,…”
A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics (2023, unknown): “Finance [41], Education [42], and Healthcare [43].”
MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts (2023, background): “…in educational disciplines (Seo et al., 2015; Wang et al., 2017), helping analysts with logical queries about statistical data (Wu et al., 2023; Yang et al., 2023), and assisting in theorem proving and scientific discovery in advanced research fields (Taylor et al., 2022; Dong et al., 2023).”