Domain Specific Model

Domain-specific models adapt large language models (LLMs) and other foundation models to excel in particular fields, overcoming the limitations of general-purpose models which may not capture nuanced domain-specific patterns. Current research focuses on techniques like fine-tuning, prompt engineering, and model merging to create these specialized models, often leveraging architectures such as transformers and conformers, and exploring data augmentation methods to address data scarcity issues. This work is significant because it improves the accuracy and reliability of AI systems in various sectors, from healthcare and agriculture to high-performance computing and standardization, by tailoring models to the unique characteristics of their respective domains.