Enhancing Virtual Human Interactions by Designing a Real-Time Dialog Filter for Mitigating Nonsensical Responses - de Siqueira, Alexandre Gomes; Yao, Heng; Bloch-elkouby, Sarah; Lawrence, Olivia; Sarli, Giuseppe; Rogers, Megan L.; Venkatakrishnan, Rohith; Venkatakrishnan, Roshan; Galynker, Igor; Lok, Benjamin (Anais do Simpósio de Realidade Virtual e Aumentada (SVR), 2024)
Virtual Humans (VHs) are crucial in facilitating discussions on sensitive topics and training interpersonal interactions. However, conversational errors, like nonsensical responses, challenge VH simulation effectiveness. This paper explores real-time dialog filters to detect such undesired exchanges. We employ a five-step prompt design iteratively and leverage OpenAI’s GPT large language model to demonstrate feasibility. Our filter distinguishes meaningful from nonsensical responses generated by a rule-based system, achieving high F1 scores (0.84) and accuracy (0.78). Comparison with human-expert classifications validates its efficacy. Filtering nonsensical responses ensures coherent and relevant interactions, significantly enhancing efficacy. This study underscores how leveraging large language models can refine existing VH systems and improve virtual human dialogues.