NLM DIR Seminar Schedule

Abstract:

In this talk, I will present our experience of applying Large Language Models (LLMs) to biomedicine at the BioNLP group. I will first briefly introduce some basics of LLMs, including auto-regressive language modeling, scaling, alignment, few-shot learning, and chain-of-though reasoning. I will share a case study on biomedical question answering for better understanding of these concepts. Despite their great successes, LLMs are known to hallucinate confident-sounding but inaccurate content. In the second part, I will introduce two approaches that augment LLMs to reduce hallucinations in biomedicine, namely retrieval augmentation and tool augmentation. For the former, I will talk about our perspective on how LLMs will impact information seeking from biomedical literature. For the latter, I will present our GeneGPT work for teaching LLMs to use NCBI Web APIs. Finally, with the knowledge gained from the first two parts, I will share our application research, TrialGPT, for patient-to-trial matching with LLMs.