Role of Instruction-Tuning and Prompt Engineering in Clinical Domain - MedAI 125

Explore the critical role of instruction-tuning and prompt engineering in advancing Clinical Natural Language Processing (NLP) in this 59-minute talk by Mihir Parmar, a Ph.D. student at Arizona State University and Research Associate at Mayo Clinic. Discover how In-BoXBART leverages instruction-tuning to enhance performance across multiple biomedical tasks and learn about a collaborative Large Language Model (LLM) framework that improves the efficiency and accuracy of systematic reviews in oncology. Gain insights into how these NLP techniques can optimize clinical processes and evidence-based practices. Parmar, an accomplished researcher with publications in top-tier NLP conferences, shares his expertise in pioneering instruction-tuning in the biomedical domain, analyzing instruction impact on model performance, and exploring LLMs' capabilities in question decomposition, program synthesis, and reasoning.