FinGPT: Democratizing Internet-scale Data for Financial Large Language Models (2307.10485v2)

Abstract: LLMs have demonstrated remarkable proficiency in understanding and generating human-like texts, which may potentially revolutionize the finance industry. However, existing LLMs often fall short in the financial field, which is mainly attributed to the disparities between general text data and financial text data. Unfortunately, there is only a limited number of financial text datasets available, and BloombergGPT, the first financial LLM (FinLLM), is close-sourced (only the training logs were released). In light of this, we aim to democratize Internet-scale financial data for LLMs, which is an open challenge due to diverse data sources, low signal-to-noise ratio, and high time-validity. To address the challenges, we introduce an open-sourced and data-centric framework, Financial Generative Pre-trained Transformer (FinGPT), that automates the collection and curation of real-time financial data from 34 diverse sources on the Internet, providing researchers and practitioners with accessible and transparent resources to develop their FinLLMs. Additionally, we propose a simple yet effective strategy for fine-tuning FinLLM using the inherent feedback from the market, dubbed Reinforcement Learning with Stock Prices (RLSP). We also adopt the Low-rank Adaptation (LoRA, QLoRA) method that enables users to customize their own FinLLMs from general-purpose LLMs at a low cost. Finally, we showcase several FinGPT applications, including robo-advisor, sentiment analysis for algorithmic trading, and low-code development. FinGPT aims to democratize FinLLMs, stimulate innovation, and unlock new opportunities in open finance. The codes have been open-sourced.

FinGPT: An Open-Source Framework for Financial LLMs

The paper "Data-centric FinGPT: Democratizing Internet-scale Data for Financial LLMs" addresses the critical challenge of adapting LLMs for financial applications. Traditional LLMs, while capable of processing general text, often fail to effectively handle the nuances of financial data. This paper introduces FinGPT, an open-source framework designed to facilitate the development of Financial LLMs (FinLLMs) by democratizing access to vast amounts of financial data and offering tools for efficient model adaptation.

The core objective of FinGPT is to bridge the gap between general-purpose LLMs and the specific requirements of the financial domain. This is achieved by tackling three primary challenges: diverse data sources, data quality issues, and high time-validity inherent in financial markets. The framework provides a systematic approach to dynamically collect and curate financial data from over 34 sources, ensuring high-quality input for LLM training.

Key Contributions

1. Automated Data Curation and Access: FinGPT introduces an automated pipeline to gather and curate financial data, thus democratizing access to what is typically a resource-intensive process dominated by institutions with specialized access. The framework aggregates data from news, social media, company filings, and research datasets, offering a single interface for users to fetch relevant financial data easily.
2. Lightweight Model Adaptation: The framework leverages innovative techniques such as Reinforcement Learning with Stock Prices (RLSP) and Low-rank Adaptation (LoRA), allowing for cost-effective customization of LLMs to specific financial tasks. By employing RLSP, the model uses market data as reinforcement signals, significantly reducing dependency on human-labeling efforts for training data.
3. Empirical Applications and Demonstrations: The paper showcases the applicability of FinGPT in practical scenarios, including robo-advisors, quantitative trading, and low-code development platforms. These use cases highlight the versatility and potential enhancements in financial decision-making processes enabled by FinGPT.

Implications and Future Directions

The introduction of FinGPT represents a significant step towards accessible and transparent financial modeling resources. By decentralizing data accessibility and offering tools for efficient LLM adaptation, the framework unlocks new research avenues in financial AI and enhances the ability of firms to leverage LLMs without prohibitive costs.

Future research could explore expanding data sources, enhancing fine-tuning methodologies, and developing domain-specific heuristics for further aligning model outputs with financial realities. The focus on open-source collaboration will likely stimulate innovation across financial technologies, promoting inclusive growth within the sector.

In conclusion, FinGPT provides a structured path for transforming LLMs into powerful tools explicitly designed for financial analytics, fostering a community-driven approach to refining AI for finance. By prioritizing data accessibility and adaptation efficiency, it sets the stage for more dynamic, reliable, and cost-efficient financial modeling solutions.