  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.
