Simulating Patient Oral Dialogues: A Study on Naturalness and Coherence of Conditioned Large Language Models

The demand for mental health services has outpaced available resources, resulting in long wait times for patients. A potential solution is to use virtual agents that perform motivational interviews. These agents can be rule-based, requiring expert knowledge, or data-driven, needing large datasets for training, which are often hard to obtain. Patient simulation can generate synthetic data as an alternative. Traditionally, this involved template utterances with a dialog manager or uncontrollable black box large language models LLMs. This study proposes a hybrid approach, leveraging both methods to see if LLMs can follow instructed dialog acts while generating natural, coherent utterances. Our study shows that the language model adheres to given conditions and that conditioning on dialog improves the naturalness and coherence of generated utterances, validating our approach for simulating patient responses.