Domain Specific Language Model

Domain-specific language models (DSLM) aim to improve the performance and efficiency of large language models (LLMs) by tailoring them to specific domains, such as medicine, finance, or sports. Current research focuses on efficient training methods for DSLMs, including techniques like knowledge expansion, adapter modules, and the use of specialized architectures like RWKV and Transformer variants. These models are proving valuable for various applications, including improved information extraction, content generation, and enhanced performance on domain-specific tasks, ultimately advancing natural language processing capabilities within specialized fields.