Domain Specific Text Classification

Domain-specific text classification aims to accurately categorize text within a particular field, overcoming challenges like limited labeled data and the high cost of annotation. Current research focuses on leveraging large and small language models (LLMs and SLMs), often employing techniques like prompt engineering and few-shot learning to improve efficiency and accuracy, even exploring the use of LLMs for data augmentation or selection. These advancements are significant because they enable the development of more effective and cost-efficient classification models for various applications, ranging from healthcare and supply chain management to large-scale IT systems, ultimately improving the accessibility and utility of NLP in specialized domains.