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Large language models (LLMs) have demonstrated remarkable proficiency inunderstanding and generating human-like texts, which may potentiallyrevolutionize the finance industry. However, existing LLMs often fall short inthe financial field, which is mainly attributed to the disparities betweengeneral text data and financial text data. Unfortunately, there is only alimited number of financial text datasets available, and BloombergGPT, thefirst financial LLM (FinLLM), is close-sourced (only the training logs werereleased). In light of this, we aim to democratize Internet-scale financialdata for LLMs, which is an open challenge due to diverse data sources, lowsignal-to-noise ratio, and high time-validity. To address the challenges, weintroduce an open-sourced and data-centric framework, Financial GenerativePre-trained Transformer (FinGPT), that automates the collection and curation ofreal-time financial data from 34 diverse sources on the Internet, providingresearchers and practitioners with accessible and transparent resources todevelop their FinLLMs. Additionally, we propose a simple yet effective strategyfor fine-tuning FinLLM using the inherent feedback from the market, dubbedReinforcement Learning with Stock Prices (RLSP). We also adopt the Low-rankAdaptation (LoRA, QLoRA) method that enables users to customize their ownFinLLMs from general-purpose LLMs at a low cost. Finally, we showcase severalFinGPT applications, including robo-advisor, sentiment analysis for algorithmictrading, and low-code development. FinGPT aims to democratize FinLLMs,stimulate innovation, and unlock new opportunities in open finance. The codeshave been open-sourced.

FinGPT: Democratizing Internet-scale Data for Financial Large Language Models