In a corresponding step, during the prompt engineering
process, we also prompt the model to select one from the pos-
itive, negative, and neutral outputs. This strategy ensures op-
timal utilization of the pre-trained information. By deploying
the Low-Rank Adaptation (LoRA) of LLMs [Hu et al., 2021;
Dettmers et al., 2023], we manage to reduce the number of
trainable parameters from 6.17 billion to a mere 3.67 million.
Fine-tuning via Reinforcement Learning on Stock Prices
(RLSP)
Similarly, we can substitute Reinforcement Learning on
Stock Prices (RLSP) for Reinforcement Learning on Human
feedback, as utilized by ChatGPT. The reasoning behind this
substitution is that stock prices offer a quantifiable, objective
metric that reflects market sentiment in response to news and
events. This makes it a robust, real-time feedback mechanism
for training our model.
FinGPT-Open-SourceFinancialLargeLanguageModels