# Cross-Format Retrieval-Augmented Generation in XR with LLMs for Context-Aware Maintenance Assistance

arXiv: https://arxiv.org/abs/2502.15604 · HTML: https://mlsanity.com/paper/2502.15604/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation.
- Builds on 15 of its 27 references that are in the map.
- First posted on arXiv 21 Feb 2025.

## Placed in

- Retrieval-augmented generation / [RAG pipeline design and evaluation](https://mlsanity.com/rag/rag-pipeline-design-and-evaluation/)

## Builds on (most cited first, 15 of 15)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2405.06211/) (2024)
- [Large Language Models Meet NLP: A Survey](https://mlsanity.com/paper/2405.12819/) (2024)
- [RAG-Driver: Generalisable Driving Explanations with Retrieval-Augmented In-Context Learning in Multi-Modal Large Language Model](https://mlsanity.com/paper/2402.10828/) (2024)
- [GazePointAR: A Context-Aware Multimodal Voice Assistant for Pronoun Disambiguation in Wearable Augmented Reality](https://mlsanity.com/paper/2404.08213/) (2024)
- [Multimodal Conversational AI: A Survey of Datasets and Approaches](https://mlsanity.com/paper/2205.06907/) (2022)
- [REAPER: Reasoning based Retrieval Planning for Complex RAG Systems](https://mlsanity.com/paper/2407.18553/) (2024)
- [A Comparative Study of PDF Parsing Tools Across Diverse Document Categories](https://mlsanity.com/paper/2410.09871/) (2024)
- [Dynamic Contexts for Generating Suggestion Questions in RAG Based Conversational Systems](https://mlsanity.com/paper/2403.11413/) (2024)
- [Large Language Models for JSON Schema Discovery](https://mlsanity.com/paper/2407.03286/) (2024)
- [AI-Powered Interfaces for Extended Reality to support Remote Maintenance](https://mlsanity.com/paper/2306.16961/) (2023)
- [Evaluation of AI-Supported Input Methods in Augmented Reality Environment](https://mlsanity.com/paper/2306.17132/) (2023)
- [User Experience Evaluation of AR Assisted Industrial Maintenance and Support Applications](https://mlsanity.com/paper/2410.17348/) (2024)

## Built on by (most cited first, 3 of 3)

- [Generative Augmented Reality: Paradigms, Technologies, and Future Applications](https://mlsanity.com/paper/2511.16783/) (2025)
- [A Retrieval-Augmented Language Assistant for Unmanned Aircraft Safety Assessment and Regulatory Compliance](https://mlsanity.com/paper/2603.09999/) (2026)
- [How Much Does It Cost to Answer My Question? Benchmarking Cloud VLM-based VQA Systems](https://mlsanity.com/paper/2608.07861/) (2026)

## Abstract (arXiv)

This paper presents a detailed evaluation of a Retrieval-Augmented Generation (RAG) system that integrates large language models (LLMs) to enhance information retrieval and instruction generation for maintenance personnel across diverse data formats. We assessed the performance of eight LLMs, emphasizing key metrics such as response speed and accuracy, which were quantified using BLEU and METEOR scores. Our findings reveal that advanced models like GPT-4 and GPT-4o-mini significantly outperform their counterparts, particularly when addressing complex queries requiring multi-format data integration. The results validate the system's ability to deliver timely and accurate responses, highlighting the potential of RAG frameworks to optimize maintenance operations. Future research will focus on refining retrieval techniques for these models and enhancing response generation, particularly for intricate scenarios, ultimately improving the system's practical applicability in dynamic real-world environments.