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Related Experiment Video

Updated: Jun 29, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Named entity recognition for coal mining machine assembly based on domain large language model.

Yunrui Wang1, Xintong Sui2, Zhaoyang Zheng2

  • 1School of Mechanical Engineering, Xi'an University of Science and T echnology, Xi'an, 710054, China. wangyunr2001@xust.edu.cn.

Scientific Reports
|June 14, 2026
PubMed
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This study introduces a domain-specific large language model for Named Entity Recognition (NER) in coal mining machine assembly. The method significantly improves NER accuracy, aiding workers in understanding complex assembly data.

Area of Science:

  • Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Coal mining machine assembly generates complex, unstructured text data, hindering operational efficiency.
  • Traditional Named Entity Recognition (NER) models struggle with specialized, scarce datasets in this domain.

Purpose of the Study:

  • To develop a domain-specific large language model (LLM) for improved NER in coal mining machine assembly.
  • To enhance the understanding and processing of complex assembly-related textual data.

Main Methods:

  • Comparative analysis of foundational LLMs to select the most suitable for coal mining domain data.
  • Application of QLoRA (Quantized Low-Rank Adaptation) fine-tuning to optimize LLM parameters efficiently.
  • Fine-tuning and evaluation on a real-world coal mining machine assembly dataset.
Keywords:
Coal mining machine assemblyLarge language modelsNamed entity recognitionQLoRA

Related Experiment Videos

Last Updated: Jun 29, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Main Results:

  • Significant performance improvement in NER tasks, with BLEU-4 score increasing from 6.1225 to 65.8013.
  • Achieved a high F1-Score of 0.893 for the NER task.
  • Demonstrated effective handling of complex assembly data with reduced computational resource demands.

Conclusions:

  • The proposed domain-specific LLM with QLoRA fine-tuning enhances NER accuracy for coal mining machine assembly.
  • This advancement is crucial for assisting workers in comprehending intricate assembly information and boosting efficiency.