Domain-specific knowledge and context in large language models: challenges, concerns, and solutions

Abstract

Large language models (LLMs) are ubiquitous today with major usage in the fields of industry, research, and academia. LLMs involve unsupervised learning with large natural language data, obtained mostly from the internet. There are several challenges that arise because of these data sources. One such challenge is with respect to domain-specific knowledge and context. This paper deals with the major challenges faced by LLMs due to data sources, such as, lack of domain expertise, understanding specialized terminology, contextual understanding, data bias, and the limitations of transfer learning. This paper also discusses some solutions for the mitigation of these challenges such as pre-training LLMs on domain-specific corpora, expert annotations, improving transformer models with enhanced attention mechanisms, memory-augmented models, context-aware loss functions, balanced datasets, and the use of knowledge distillation techniques.