Financial Large Language Model

Financial Large Language Models (FinLLMs) aim to leverage the power of LLMs for financial tasks, improving accuracy and efficiency in areas like asset pricing, stock prediction, and financial analysis. Current research focuses on adapting existing LLMs to the financial domain through techniques like instruction tuning, continual pretraining, and model merging, often incorporating multimodal data (text, numbers, images). These models show promise in outperforming traditional methods and even proprietary models on specific benchmarks, potentially revolutionizing financial analysis and decision-making. However, challenges remain in addressing biases, ensuring interpretability, and handling the nuances of financial data across different languages.