Yong Keat Tan

Balancing Accuracy and Efficiency in Multi-Turn Intent Classification for LLM-Powered Dialog Systems in Production

Accurate multi-turn intent classification is critical for advancing conversational AI systems but remains challenging due to limited datasets and complex contextual dependencies across dialogue turns. This paper presents two novel approaches leveraging Large Language Models (LLMs) to enhance scalabi