FinGPT: Open-Source Financial Large Language Models (2306.06031v1)

Abstract: 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 LLM, 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}

FinGPT: Open-Source Financial LLMs

The paper "FinGPT: Open-Source Financial LLMs" addresses significant challenges in applying LLMs to the finance sector. It presents an accessible alternative, FinGPT, to proprietary models like BloombergGPT by emphasizing a data-centric approach and open-source framework for deploying financial LLMs.

Overview and Contributions

FinGPT distinguishes itself by adopting a comprehensive end-to-end open-source framework designed for financial LLMs (FinLLMs). Key contributions include the democratization of financial data access, enabling researchers and practitioners to develop, fine-tune, and utilize FinGPT without prohibitive costs. The framework is structured into four principal layers:

1. Data Source Layer: This layer captures real-time financial data from diverse sources, such as news, social media, and company filings, to ensure comprehensive market coverage.
2. Data Engineering Layer: It facilitates real-time processing with sophisticated NLP techniques tailored to address the inherent challenges of high temporal sensitivity and low signal-to-noise ratios typical in financial data.
3. LLMs Layer: This layer enables the model to stay relevant with evolving financial landscapes through fine-tuning strategies, including Low-Rank Adaptation (LoRA) and Reinforcement Learning from Stock Prices (RLSP).
4. Application Layer: Demonstrates FinGPT’s applicability across financial services, such as robo-advising, quantitative trading, and low-code development platforms.

Strong Numerical Results and Bold Claims

FinGPT addresses financial modeling costs head-on. For instance, while training models like BloombergGPT may require over a million GPU hours, estimated at \$3 million, FinGPT’s adaptation process is projected to cost under \$300 per training session. This cost-effective strategy is achieved through leveraging existing LLMs and fine-tuning them using lightweight techniques like LoRA.

Theoretical and Practical Implications

FinGPT implies substantial theoretical advancements in the financial application of LLMs by providing a robust, transparent, and adaptable open-source model. Practically, it offers enhanced access to financial modeling tools, thus potentially redefining how individuals and institutions utilize AI in financial decision-making processes.

Future Developments in AI and Finance

The future direction articulated for FinLLMs integrates individual customization, signifying an era where personalized financial advice becomes feasible for a broader audience. The open-source nature and the focus on low-cost adaptation promote a democratization of financial AI technology, facilitating broader participation and accelerating research in the field.

Conclusion

FinGPT represents a critical step forward in the open-source AI landscape, particularly within finance. By addressing the pressing challenges of data access and model adaptability while lowering cost barriers, it offers a viable alternative to proprietary models. This initiative exemplifies how open-source communities and frameworks can foster innovation, collaboration, and accessibility in specialized domains like finance.