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Related Experiment Video

Updated: Jan 6, 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

980

Optimizing software engineering English translation using an enhanced Grey Wolf Optimization with self-attention and

Fang Yuan1, Yao Liu2, Yongfeng Ju3

  • 1School of Foreign Languages, Huaiyin Normal University, Huai'an, China. iris@hytc.edu.cn.

Scientific Reports
|October 10, 2025
PubMed
Summary

This study introduces an Adaptive Grey Wolf Optimization with Self-Attention and LSTM (AGWO-SALSTM) model for enhanced machine translation. The novel approach significantly improves Chinese-to-English translation accuracy and efficiency in specialized domains.

Keywords:
Attention mechanismBi-LSTMGrey Wolf OptimizationSoftware engineering

Related Experiment Videos

Last Updated: Jan 6, 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

980

Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • Traditional neural machine translation models like Transformer and LSTM face challenges in dynamic hyperparameter optimization and domain adaptation.
  • Suboptimal accuracy and efficiency hinder the application of existing models in specialized fields such as software engineering.
  • Bridging language gaps effectively requires advanced machine translation solutions.

Purpose of the Study:

  • To propose an enhanced machine translation model, Adaptive Grey Wolf Optimization with Self-Attention and LSTM (AGWO-SALSTM), for improved Chinese-to-English translation.
  • To dynamically optimize hyperparameters and enhance contextual understanding for greater translation accuracy and efficiency.
  • To address the limitations of traditional models in specialized domains.

Main Methods:

  • Developed the AGWO-SALSTM model, integrating adaptive Grey Wolf Optimization (AGWO) for hyperparameter tuning (learning rates, attention weights, network configurations).
  • Incorporated self-attention mechanisms and bidirectional Long Short-Term Memory (LSTM) networks to improve contextual understanding and sequential data processing.
  • Validated the AGWO-SALSTM model against Transformer, LSTM-Seq2Seq, and MT5 baselines using PARACRAWL, WMT, UM-Corpus, and OPUS datasets.

Main Results:

  • AGWO-SALSTM demonstrated superior performance over baseline models across all tested datasets.
  • Achieved an average translation accuracy of 95.56%, significantly outperforming MT5 (90.94%).
  • Required fewer iterations (16-20) for convergence compared to the Transformer model (up to 57), indicating enhanced efficiency.

Conclusions:

  • The AGWO-SALSTM model offers a significant advancement in machine translation accuracy and efficiency, particularly for Chinese-to-English translation in specialized domains.
  • Dynamic hyperparameter optimization via AGWO and enhanced contextual processing through self-attention and LSTM are key to the model's improved performance.
  • The proposed model effectively overcomes the limitations of traditional neural machine translation approaches, paving the way for more robust and efficient language bridging solutions.