Eskandari, MohammadIndukuri, MuraliLukin, Stephanie M.Matuszek, Cynthia2025-04-012025-04-012025-02-13Eskandari, Mohammad, Murali Indukuri, Stephanie M. Lukin, and Cynthia Matuszek. "LLM-Supported Safety Annotation in High-Risk Environments," 2025. https://openreview.net/forum?id=Ewg3WsMBRv.http://hdl.handle.net/11603/37889HRI 2025 Workshop VAM SubmissionThis paper explores how large language model-based robots assist in detecting anomalies in high-risk environments and how users perceive their usability and reliability in a safe virtual environment. We present a system where a robot using a state-of-the-art vision-language model autonomously annotates potential hazards in a virtual world. The system provides users with contextual safety information via a VR interface. We conducted a user study to evaluate the system's performance across metrics such as trust, user satisfaction, and efficiency. Results demonstrated high user satisfaction and clear hazard communication, while trust remained moderate.9 pagesen-USThis work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.Public Domainhttps://creativecommons.org/publicdomain/mark/1.0/Human-Robot InteractionUMBC Interactive Robotics and Language LabSafety AnnotationHazard DetectionVirtual RealityLarge Language ModelsUMBC Interactive Robotics and Language LabText