mlsanityData to 14 Oct 2026

RATE: An LLM-Powered Retrieval Augmented Generation Technology-Extraction Pipeline

arXiv 2507.21125 ↗First posted 19 Jul 2025cs.IR cs.AI cs.LGcited by 0

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

In an era of radical technology transformations, technology maps play a crucial role in enhancing decision making. These maps heavily rely on automated methods of technology extraction. This paper introduces Retrieval Augmented Technology Extraction (RATE), a Large Language Model (LLM) based pipeline for automated technology extraction from scientific literature. RATE combines Retrieval Augmented Generation (RAG) with multi-definition LLM-based validation. This hybrid method results in high recall in candidate generation alongside with high precision in candidate filtering. While the pipeline is designed to be general and widely applicable, we demonstrate its use on 678 research articles focused on Brain-Computer Interfaces (BCIs) and Extended Reality (XR) as a case study. Consequently, The validated technology terms by RATE were mapped into a co-occurrence network, revealing thematic clusters and structural features of the research landscape. For the purpose of evaluation, a gold standard dataset of technologies in 70 selected random articles had been curated by the experts. In addition, a technology extraction model based on Bidirectional Encoder Representations of Transformers (BERT) was used as a comparative method. RATE achieved F1-score of 91.27%, Significantly outperforming BERT with F1-score of 53.73%. Our findings highlight the promise of definition-driven LLM methods for technology extraction and mapping. They also offer new insights into emerging trends within the BCI-XR field. The source code is available https://github.com/AryaAftab/RATE

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 9 of its 40 references that are in the map. First posted on arXiv 19 Jul 2025.

Builds on

9 of 40 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Retrieval-Augmented Generation for Large Language Models: A Survey18 Dec

Other references · 8

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2023A Survey of Large Language Modelscited by 2,576
  3. 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,218
  4. 2024Large Language Models: A Surveycited by 525
  5. 2021NER-BERT: A Pre-trained Model for Low-Resource Entity Taggingcited by 21
  6. 2025Understanding and Mitigating Bias Inheritance in LLM-based Data Augmentation on Downstream Taskscited by 9
  7. 2025Large (Vision) Language Models are Unsupervised In-Context Learnerscited by 6
  8. 2021Taxonomy of Virtual and Augmented Reality Applications in Educationcited by 1

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025A Systematic Literature Review of Retrieval-Augmented Generation: Techniques, Metrics, and Challenges
  2. 2024Deploying Large Language Models With Retrieval Augmented Generation
  3. 2025Retrieval-Augmented Generation in Industry: An Interview Study on Use Cases, Requirements, Challenges, and Evaluation
  4. 2025NANOGPT: A Query-Driven Large Language Model Retrieval-Augmented Generation System for Nanotechnology Research
  5. 2025Technology Mapping with Large Language Models
  6. 2024LitLLM: A Toolkit for Scientific Literature Review
  7. 2025FutureGen: A RAG-based Approach to Generate the Future Work of Scientific Article
  8. 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey