Leveraging NLP in Finance: A Synergistic Approach Using Large Language Models and Chain-of-Thought Reasoning
Yong Deng, Xintong Zhang, Danping Zhou, Dequan Zhang, Boya Huang · 2024
In this paper, we explore the synergy between large language models (LLMs) and chain-of-thought(CoT) reasoning in the context of extracting valuable insights from public data sources for financial applications. We emphasize how this integration can significantly enhance the financial industry's ability to process and interpret vast amounts of unstructured data, leading to more informed decision-making and strategic advantages. By combining LLMs' extensive knowledge and natural language generation capabilities with the logical reasoning of CoT processes, we demonstrate the potential for extracting precise and actionable financial information from publicly available resources. This paper also delves into the challenges and limitations encountered in this integration and suggests future research directions to further streamline and optimize these algorithms for real-world financial use cases. Overall, our discussion underscores the transformative power of combining advanced NLP techniques with financial expertise to unlock the full potential of public data for financial gain.