Enterprise E-Mail Classification Using Instruction-Following Large Language Models

Enterprise e-mail corpora contain heterogeneous and domain-specific content that poses challenges for conventional supervised Natural Language Processing (NLP) approaches due to class imbalance, evolving terminology, and limited labeled data. This study examines the use of instruction-following Large Language Models (LLMs) for enterprise e-mail classification under realistic operational conditions. The study evaluates instruction-based classification and semantic enrichment derived from distributional similarity as two complementary approaches for distinguishing technical from nontechnical messages. The approaches are assessed on a large-scale enterprise e-mail corpus and validated using a manually annotated subset. The results indicate that instruction-following LLMs provide stable contextual reasoning across diverse message structures, while semantic enrichment improves coverage of previously unseen technical expressions. Overall, the study presents an applied NLP framework for enterprise e-mail classification, with attention to interpretability, scalability, and robustness in real-world organizational settings.