MedAI

Title: Role of Instruction-Tuning and Prompt Engineering in Clinical Domain

Speaker: Mihir Parmar

Abstract:
In this talk, I will discuss the pivotal role of instruction-tuning and prompt engineering in advancing Clinical NLP. I will cover how our In-BoXBART leverages instruction-tuning to improve performance across multiple biomedical tasks, and how a collaborative LLM framework enhances the efficiency and accuracy of systematic reviews in oncology. These studies collectively demonstrate how these NLP techniques can optimize clinical processes and evidence-based practices.

Speaker Bio:
Mihir is a Ph.D. student at Arizona State University and a Research Associate at Mayo Clinic. His research has been published in top-tier NLP conferences such as ACL, EMNLP, NAACL, and EACL, where he received the "Outstanding Paper Award" at EACL 2023. His work focuses on pioneering instruction-tuning in the biomedical domain, analyzing the impact of various instructions on model performance, and exploring LLMs' capabilities in question decomposition, program synthesis, and reasoning. Additionally, he has industry experience as a research scientist intern at Adobe Research (Summer 2023) and the AI Innovation Lab at Novartis (Summer 2022).

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Amara Tariq (  / amara-tariq-475815158  )
Avisha Das (https://dasavisha.github.io/)