Mitigating Response Delays in Free-Form Conversations with LLM-powered Intelligent Virtual Agents

dc.contributor.authorMaslych, Mykolaen
dc.contributor.authorKatebi, Mohammadrezaen
dc.contributor.authorLee, Christopheren
dc.contributor.authorHmaiti, Yahyaen
dc.contributor.authorGhasemaghaei, Amirpouyaen
dc.contributor.authorPumarada, Christianen
dc.contributor.authorPalmer, Janneeseen
dc.contributor.authorSegarra Martinez, Estebanen
dc.contributor.authorEmporio, Marcoen
dc.contributor.authorSnipes, Warrenen
dc.contributor.authorMcMahan, Ryan P.en
dc.contributor.authorLaViola Jr., Joseph J.en
dc.date.accessioned2025-08-06T17:23:38Zen
dc.date.available2025-08-06T17:23:38Zen
dc.date.issued2025-07-08en
dc.date.updated2025-08-01T07:51:30Zen
dc.description.abstractWe investigated the challenges of mitigating response delays in free-form conversations with virtual agents powered by Large Language Models (LLMs) within Virtual Reality (VR). For this, we used conversational fillers, such as gestures and verbal cues, to bridge delays between user input and system responses and evaluate their effectiveness across various latency levels and interaction scenarios. We found that latency above 4 seconds degrades quality of experience, while natural conversational fillers improve perceived response time, especially in high-delay conditions. Our findings provide insights for practitioners and researchers to optimize user engagement whenever conversational systems’ responses are delayed by network limitations or slow hardware. We also contribute an open-source pipeline that streamlines deploying conversational agents in virtual environments.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1145/3719160.3736636en
dc.identifier.urihttps://hdl.handle.net/10919/136979en
dc.language.isoenen
dc.publisherACMen
dc.rightsIn Copyright (InC)en
dc.rights.holderThe author(s)en
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.titleMitigating Response Delays in Free-Form Conversations with LLM-powered Intelligent Virtual Agentsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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