Domain Specific Large Language Model

Domain-specific large language models (LLMs) aim to overcome the limitations of general-purpose LLMs by tailoring their knowledge and capabilities to specific domains like finance, medicine, or law. Current research focuses on efficient fine-tuning methods, including multi-task learning and techniques like LoRA, to enhance performance while minimizing computational costs and mitigating catastrophic forgetting. These advancements are significant because they enable the creation of more accurate and efficient LLMs for specialized applications, improving knowledge transfer, accelerating research, and enhancing decision-making in various fields.