My master's thesis focuses on enhancing Conversational AI through Intent Recognition using LLMs and Generative AI.
The rapid advancement of Conversational AI systems has transformed human-computer interaction, enabling applications ranging from virtual assistants to customer support. However, accurately understanding and responding to user queries remains a significant challenge, particularly in identifying the under lying intent. This thesis explores intent recognition as a corner stone for enhancing conversational systems by utilizing LLMs and Generative AI.
Key Areas of Research:
Taxonomy-based intent recognition.
Query reformulation using AI models.
Comparison of AI-driven vs traditional methods.
Application in real-world chatbot assistants.
Image taken from Cognizant Mobility.