Big language modeling in financial markets: present and future: One study overview

With the breakthrough of large language models (LLMs) represented by GPT and BERT in the field of natural language processing, they provide a new paradigm for financial prediction and analysis by processing unstructured textual data (e.g., news, financial reports, and social media opinions). Studies have shown that LLMs have demonstrated significant advantages in areas such as customer service, risk management, and investment decision. Some models (e.g., BloombergGPT) have achieved a balance between specialized capabilities and general performance through hybrid data training and vertical fine-tuning, which provides a paradigm for the development of large language models in current and future verticals. However, LLMs still face challenges such as insufficient understanding of specialized terminology, data privacy risk, lack of model interpretability and high-frequency transaction latency in financial scenarios. Through the research review, this paper further proposes to deal with the above problems through the technical paths of vertical model development, federated learning, and interpretability enhancement, and emphasizes the need for continuous exploration in the future in the areas of large model multimodalization , regulatory synergy , and financial inclusion deepening, in order to promote the scaled landing and value release of LLMs in the financial domain. By analyzing and discussing the history of LLMs, its core technology, current applications and problems in the financial industry, this paper shows that big language models have a broad development potential in the financial field, and are one of the core driving forces for innovation of the future the industry.