Exploring Emotion-Sensitive LLM-Based Conversational AI
This work addresses improving user satisfaction in customer service chatbots, but it is incremental as it builds on existing emotion-sensitive approaches without introducing a new paradigm.
The study tackled the problem of conversational AI chatbots lacking authenticity and competence in customer service by comparing emotion-sensitive and emotion-insensitive LLM-based chatbots with 30 participants, finding that the emotion-sensitive chatbot led to higher perceptions of trustworthiness and competence without affecting issue resolution rates.
Conversational AI chatbots have become increasingly common within the customer service industry. Despite improvements in their emotional development, they often lack the authenticity of real customer service interactions or the competence of service providers. By comparing emotion-sensitive and emotion-insensitive LLM-based chatbots across 30 participants, we aim to explore how emotional sensitivity in chatbots influences perceived competence and overall customer satisfaction in service interactions. Additionally, we employ sentiment analysis techniques to analyze and interpret the emotional content of user inputs. We highlight that perceptions of chatbot trustworthiness and competence were higher in the case of the emotion-sensitive chatbot, even if issue resolution rates were not affected. We discuss implications of improved user satisfaction from emotion-sensitive chatbots and potential applications in support services.