Domain Specific

Domain-specific adaptation of large language models (LLMs) focuses on enhancing their performance and reliability within specialized fields by overcoming limitations stemming from data scarcity and domain-specific terminology. Current research emphasizes developing effective methods for data curation, including synthetic data generation and techniques like knowledge distillation to transfer knowledge from domain-specific to general-purpose models, alongside novel architectures like graph-oriented databases for improved performance and maintenance. This work is crucial for broadening the applicability of LLMs to diverse sectors, improving efficiency in areas like finance, healthcare, and scientific research, and addressing concerns about bias and hallucination in sensitive domains.