Embedding Large Language Models into Extended Reality: Opportunities and Challenges for Inclusion, Engagement, and Privacy
CoRR(2024)
摘要
Recent developments in computer graphics, hardware, artificial intelligence
(AI), and human-computer interaction likely lead to extended reality (XR)
devices and setups being more pervasive. While these devices and setups provide
users with interactive, engaging, and immersive experiences with different
sensing modalities, such as eye and hand trackers, many non-player characters
are utilized in a pre-scripted way or by conventional AI techniques. In this
paper, we argue for using large language models (LLMs) in XR by embedding them
in virtual avatars or as narratives to facilitate more inclusive experiences
through prompt engineering according to user profiles and fine-tuning the LLMs
for particular purposes. We argue that such inclusion will facilitate diversity
for XR use. In addition, we believe that with the versatile conversational
capabilities of LLMs, users will engage more with XR environments, which might
help XR be more used in everyday life. Lastly, we speculate that combining the
information provided to LLM-powered environments by the users and the biometric
data obtained through the sensors might lead to novel privacy invasions. While
studying such possible privacy invasions, user privacy concerns and preferences
should also be investigated. In summary, despite some challenges, embedding
LLMs into XR is a promising and novel research area with several opportunities.
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