Unveiling the Impact of Multi-Modal Interactions on User Engagement: A Comprehensive Evaluation in AI-driven Conversations
<|reference_start|>Unveiling the Impact of Multi-Modal Interactions on User Engagement: A Comprehensive Evaluation in AI-driven Conversations: Large Language Models (LLMs) have significantly advanced user-bot interactions, enabling more complex and coherent dialogues. However, the prevalent text-only modality might not fully exploit the potential for effective user engagement. This paper explores the impact of multi-modal interactions, which incorporate images and audio alongside text, on user engagement in chatbot conversations. We conduct a comprehensive analysis using a diverse set of chatbots and real-user interaction data, employing metrics such as retention rate and conversation length to evaluate user engagement. Our findings reveal a significant enhancement in user engagement with multi-modal interactions compared to text-only dialogues. Notably, the incorporation of a third modality significantly amplifies engagement beyond the benefits observed with just two modalities. These results suggest that multi-modal interactions optimize cognitive processing and facilitate richer information comprehension. This study underscores the importance of multi-modality in chatbot design, offering valuable insights for creating more engaging and immersive AI communication experiences and informing the broader AI community about the benefits of multi-modal interactions in enhancing user engagement.<|reference_end|>
@article{zhang2024unveiling,
    title={Unveiling the Impact of Multi-Modal Interactions on User Engagement: A
  Comprehensive Evaluation in AI-driven Conversations},
    author={Lichao Zhang, Jia Yu, Shuai Zhang, Long Li, Yangyang Zhong, Guanbao
  Liang, Yuming Yan, Qing Ma, Fangsheng Weng, Fayu Pan, Jing Li, Renjun Xu and
  Zhenzhong Lan},
    journal={arXiv preprint arXiv:2406.15000},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.15000},
    primaryClass={cs.CL cs.AI}
}