AHCI RESEARCH GROUP

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Papers published in international journals,
proceedings of conferences, workshops and books.

OUR RESEARCH

Scientific Publications

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2025

Ding, S.; Chen, Y.

RAG-VR: Leveraging Retrieval-Augmented Generation for 3D Question Answering in VR Environments Proceedings Article

In: Proc. - IEEE Conf. Virtual Real. 3D User Interfaces Abstr. Workshops, VRW, pp. 131–136, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 979-833151484-6 (ISBN).

Abstract | Links | BibTeX | Tags: Ambient intelligence, Computational Linguistics, Computer interaction, Computing methodologies, Computing methodologies-Artificial intelligence-Natural language processing-Natural language generation, Computing methodology-artificial intelligence-natural language processing-natural language generation, Data handling, Formal languages, Human computer interaction, Human computer interaction (HCI), Human-centered computing, Interaction paradigm, Interaction paradigms, Language Model, Language processing, Natural language generation, Natural language processing systems, Natural languages, Virtual Reality, Word processing

@inproceedings{ding_rag-vr_2025,

title = {RAG-VR: Leveraging Retrieval-Augmented Generation for 3D Question Answering in VR Environments},

author = {S. Ding and Y. Chen},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105005140593&doi=10.1109%2fVRW66409.2025.00034&partnerID=40&md5=36dc5fef97aeea4d6e183c83ce9fcd89},

doi = {10.1109/VRW66409.2025.00034},

isbn = {979-833151484-6 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Proc. - IEEE Conf. Virtual Real. 3D User Interfaces Abstr. Workshops, VRW},

pages = {131–136},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Recent advances in large language models (LLMs) provide new opportunities for context understanding in virtual reality (VR). However, VR contexts are often highly localized and personalized, limiting the effectiveness of general-purpose LLMs. To address this challenge, we present RAG-VR, the first 3D question-answering system for VR that incorporates retrieval-augmented generation (RAG), which augments an LLM with external knowledge retrieved from a localized knowledge database to improve the answer quality. RAG-VR includes a pipeline for extracting comprehensive knowledge about virtual environments and user conditions for accurate answer generation. To ensure efficient retrieval, RAG-VR offloads the retrieval process to a nearby edge server and uses only essential information during retrieval. Moreover, we train the retriever to effectively distinguish among relevant, irrelevant, and hard-to-differentiate information in relation to questions. RAG-VR improves answer accuracy by 17.9%-41.8% and reduces end-to-end latency by 34.5%-47.3% compared with two baseline systems. © 2025 IEEE.},

keywords = {Ambient intelligence, Computational Linguistics, Computer interaction, Computing methodologies, Computing methodologies-Artificial intelligence-Natural language processing-Natural language generation, Computing methodology-artificial intelligence-natural language processing-natural language generation, Data handling, Formal languages, Human computer interaction, Human computer interaction (HCI), Human-centered computing, Interaction paradigm, Interaction paradigms, Language Model, Language processing, Natural language generation, Natural language processing systems, Natural languages, Virtual Reality, Word processing},

pubstate = {published},

tppubtype = {inproceedings}

}