Generative AI, particularly Large Language Models (LLMs), hold the promise to revolutionize various sectors, including public health. One notable application is in harnessing the power of these models to sift through and derive insights from vast amounts of unstructured data.

In this session, the presenter will share firsthand experience from participating and using LLMs in the CDC-hosted competition, "Unsupervised Wisdom: Explore Medical Narratives on Older Adult Falls."

Join us as we delve into:

How to Fine-Tune LLMs: Unpack the methodologies and techniques essential for tailoring generic LLMs to specialized tasks, and why customization is vital for specific applications like public health.

Data Acquisition with RLHF (Reinforcement Learning Human Feedback): Dive into the mechanisms of RLHF, understanding its role in training LLMs, its advantages over traditional methods, and how it ensures data quality and relevance.

Designed for a diverse audience – from civilian and defense government agency representatives to public health experts, academia, and AI practitioners – this session offers a concrete example of how cutting-edge AI can intersect with critical public sector missions.