Unveiling The Impact Of Multi-modal Interactions On User Engagement: A Comprehensive Evaluation In Ai-driven Conversations
Zhang Lichao, Yu Jia, Zhang Shuai, Li Long, Zhong Yangyang, Liang Guanbao, Yan Yuming, Ma Qing, Weng Fangsheng, Pan Fayu, Li Jing, Xu Renjun, Lan Zhenzhong. Arxiv 2024
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.
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