Domain Specificity

Domain specificity in machine learning explores whether models trained on data from a specific domain (e.g., biomedical text) outperform general-purpose models on tasks within that domain. Current research focuses on comparing domain-specific models, often using transformer architectures, against general models fine-tuned with techniques like instruction learning, and investigates the trade-offs between domain expertise and uncertainty quantification. Findings suggest that while domain-specific pre-training isn't always superior, domain-specific fine-tuning can significantly improve performance on downstream tasks, particularly with limited data, impacting the efficiency and accuracy of applications across various fields.