# Rapport-Driven Virtual Agent: Rapport Building Dialogue Strategy for Improving User Experience at First Meeting

arXiv: https://arxiv.org/abs/2406.09839 · HTML: https://mlsanity.com/paper/2406.09839/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 0 of its 26 references that are in the map.
- First posted on arXiv 14 Jun 2024.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Built on by (most cited first, 2 of 2)

- [Mixed Signals: Understanding Model Disagreement in Multimodal Empathy Detection](https://mlsanity.com/paper/2505.13979/) (2025)
- [Dynamic In-Group Persona Generation for Enhancing Human-AI Rapport](https://mlsanity.com/paper/2606.18256/) (2026)

## Abstract (arXiv)

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.