  Rapport is known as a conversational aspect focusing on relationship
building, which influences outcomes in collaborative tasks. This study aims to
establish human-agent rapport through small talk by using a rapport-building
strategy. We implemented this strategy for the virtual agents based on dialogue
strategies by prompting a large language model (LLM). In particular, we
utilized two dialogue strategies-predefined sequence and free-form-to guide the
dialogue generation framework. We conducted analyses based on human
evaluations, examining correlations between total turn, utterance characters,
rapport score, and user experience variables: naturalness, satisfaction,
interest, engagement, and usability. We investigated correlations between
rapport score and naturalness, satisfaction, engagement, and conversation flow.
Our experimental results also indicated that using free-form to prompt the
rapport-building strategy performed the best in subjective scores.
