Domain Specific LLM

Domain-specific Large Language Models (LLMs) aim to improve the performance and efficiency of LLMs by tailoring them to particular domains, such as healthcare or legal text. Current research focuses on methods like retrieval-augmented generation (RAG), reinforcement learning for improved information extraction, and model pruning techniques to reduce computational costs while maintaining accuracy. These advancements are significant because they address the limitations of general-purpose LLMs in handling domain-specific nuances and offer cost-effective solutions for deploying LLMs in various applications.