Domain Specific Continual Pre Training

Domain-specific continual pre-training (CPT) enhances large language models (LLMs) by incrementally training them on data from specific domains, improving performance on domain-relevant tasks while preserving general knowledge. Current research focuses on optimizing data selection strategies, developing efficient algorithms for knowledge integration (e.g., model merging, soft-masking), and establishing scaling laws to guide the optimal balance between general and domain-specific data. This approach offers a cost-effective alternative to training domain-specific LLMs from scratch, impacting various fields by enabling the creation of specialized models for finance, e-commerce, mental health, and other areas with limited labeled data.