6:00 PM Seminar Begins
7:30 PM Reception
Hybrid Event:
Fordham University
McNally Amphitheater
140 West 62nd Street
New York, NY 10023
Free Registration!
For Virtual Attendees: Please select virtual instead of member type upon registration.
Abstract:
In this talk, Xiao-Yang Liu will showcase their FinGPT--an open-source counterpart of BloombergGPT, on financial regulations. In particular, the team's two-year efforts on benchmarking financial large language models, with a zooming in Financial Regulations. He will also share ongoing projects in GenAI Research on Open Finance at Columbia University.
The financial industry operates within a labyrinth of complex regulations and industry standards designed to maintain market integrity and ensure reliability in financial reporting and compliance processes. Intricate financial regulations and standards have presented significant challenges for financial professionals and organizations. Large language models (LLMs), such as GPT-4o, Llama 3.1, and DeepSeek's V3/R1 models, have shown remarkable capabilities in natural language understanding and generation, making them promising for applications in the financial sector. However, current LLMs face challenges in the domain of financial regulations and industry standards. These challenges include grasping specialized regulatory language, maintaining up-to-date knowledge of evolving regulations and industry standards, and ensuring interpretability and ethical considerations in their responses.
Bio:
Dr. Xiao-Yang Liu graduated in Electrical Engineering of Columbia University. He is part-time researcher at Lab GenAI Research on Open Finance, Columbia University, and a faculty member in RPI's CS department. His research interests include Reinforcement Learning, Large Language Models, Quantum Computing, and applications to finance. He created the popular open-source projects, FinGPT, FinRL, and ElegantRL.
