Using LLMs for Adaptive Dialogue Management

Adapted user-directed utterances using LLMs based on the user's parameters like gender, age, and sentiment, aiming to optimize user satisfaction in conversational AI systems, focusing on healthcare patient-practice interactions.

Evaluated different LLMs and open-source tools for effectiveness in utterance adaptation, in terms of speed, cost-effectiveness, and quality of the generated text based on the adaptation relevancy and adaptation adequacy.