A Comparative Framework for Intent Classification Systems: Evaluating Large Language Models versus Traditional Machine Learning in Contact Center Applications

Abstract

Modern contact centers face increasingly complex decisions when selecting appropriate technologies for intent identification systems. This article presents a systematic comparative analysis of Large Language Models (LLMs) and traditional Machine Learning (ML) approaches in contact center environments, examining their relative efficacy across various operational contexts. Through a comprehensive evaluation framework, we assess seven critical dimensions: data complexity, training requirements, performance metrics, resource utilization, customization capabilities, deployment considerations, and hybrid implementation strategies. Our findings indicate that LLMs demonstrate superior performance in scenarios involving complex linguistic patterns and contextual understanding, while traditional ML models maintain advantages in resource-constrained environments and clearly defined intent categories. We propose a novel decision framework that enables organizations to optimize their technology selection based on specific operational requirements, resource availability, and performance needs. The article contributes to both theoretical understanding and practical implementation by providing evidence-based guidelines for selecting and implementing intent identification systems. The results suggest that hybrid approaches, combining the strengths of both LLMs and traditional ML models, offer promising solutions for organizations seeking to balance sophisticated language understanding with operational efficiency These findings have significant implications for contact center automation strategies and provide a foundation for future research in adaptive intent classification systems.