How to Design, Create, and Evaluate an Instruction-Tuning Dataset for Large Language Model Training in Health Care: Tutorial From a Clinical Perspective.

ABSTRACT: High-quality data are critical in health care, forming the cornerstone for accurate diagnoses, effective treatment plans, and reliable conclusions. Similarly, high-quality datasets underpin the development and performance of large language models (LLMs). Among these, instruction-tuning datasets (ITDs) used for instruction fine-tuning have been pivotal in enhancing LLM performance and generalization capabilities across diverse tasks. This tutorial provides a comprehensive guide to designing, creating, and evaluating ITDs for health care applications. Written from a clinical perspective, it aims to make the concepts accessible to a broad audience, especially medical practitioners. Key topics include identifying useful data sources, defining the characteristics of well-designed datasets, and

SUBMITTER: Nazar W 

PROVIDER: S-EPMC11962319 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature