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Related Experiment Video

Updated: Sep 16, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Benchmarking pre-trained text embedding models in aligning built asset information.

Mehrzad Shahinmoghadam1, Ali Motamedi2

  • 1Department of Construction Engineering, École de Technologie Supérieure, Montreal, H3C 1K3, Canada. mehrzad.shahinmoghadam.1@ens.etsmtl.ca.

Scientific Reports
|July 4, 2025
PubMed
Summary
This summary is machine-generated.

This study benchmarks text embedding models for mapping built asset data. Findings show model performance varies, highlighting the need for domain-specific evaluations and adaptation techniques like instruction-tuning.

Keywords:
BenchmarkDatasetDomain-specific evaluationInformation alignmentLarge language models (LLMs)Pre-trained language modelRepresentationText embedding

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Area of Science:

  • Engineering
  • Computer Science
  • Data Science

Background:

  • Accurate mapping of built asset information to classification systems is vital for asset management and data integration.
  • Current manual mapping processes are time-consuming and rely heavily on domain experts.
  • Large language models (LLMs) offer potential for automating this mapping through text embedding.

Purpose of the Study:

  • To conduct a comprehensive evaluation of state-of-the-art text embedding models for built asset data.
  • To assess the effectiveness of these models in representing complex semantics within built asset technical terminology.
  • To provide a benchmark for future research and development in automated asset data mapping.

Main Methods:

  • Developed novel datasets derived from two prominent built asset data classification dictionaries.
  • Conducted a comparative benchmark of various text embedding models.
  • Evaluated model performance across clustering, retrieval, and reranking tasks on six proposed datasets.

Main Results:

  • Significant performance variations were observed among different text embedding models.
  • The common trend of larger models outperforming smaller ones was not consistently found.
  • Results emphasize the critical need for domain-specific evaluations rather than general performance metrics.

Conclusions:

  • Domain-specific evaluation is crucial for assessing the suitability of text embedding models for built asset data.
  • Further research into domain adaptation techniques, particularly instruction-tuning, is recommended.
  • The study's benchmarking resources are released as an open-source library to foster continued research.