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Related Experiment Video

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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A domain-specific cross-lingual semantic alignment learning model for low-resource languages.

Yurong Wang1, Min Lin2, Qitu Hu1

  • 1College of Mathematics Science, Inner Mongolia Normal University, Hohhot, 010022, Inner Mongolia, China; Center for Applied Mathematics Inner Mongolia, Hohhot, 010022, Inner Mongolia, China; Key Laboratory of Infinite-dimensional Hamiltonian Systems and Algorithmic Applications of the Ministry of Education, Hohhot, 010022, Inner Mongolia, China.

Neural Networks : the Official Journal of the International Neural Network Society
|September 28, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces CLWKD, a cross-lingual framework enhancing domain-specific data sharing for low-resource languages. It improves semantic alignment and robustness, especially for complex languages like Mongolian and Korean.

Keywords:
Cross-lingual knowledge distillationCross-lingual mappingCross-lingual multi-granularity semantic alignmentDomain-specificLow-resource languages

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Machine Learning

Background:

  • Cross-lingual semantic alignment models are crucial for multilingual domain-specific data utilization.
  • Existing methods struggle with parallel data scarcity, semantic heterogeneity, and morphological complexity, particularly in agglutinative languages.

Purpose of the Study:

  • Propose CLWKD, a novel cross-lingual mapping and knowledge distillation framework.
  • Enhance cross-lingual knowledge transfer for low-resource languages using domain-specific pretrained models.
  • Address challenges in data scarcity and morphological complexity for agglutinative languages.

Main Methods:

  • CLWKD integrates multi-granularity alignment matrices (token, word, sentence) with limited parallel data.
  • Employs multilingual embedding sharing and morphological segmentation for agglutinative languages.
  • Utilizes generator pretraining and parameter recycling for stable, efficient mapping.

Main Results:

  • CLWKD demonstrates effectiveness across medical, legal, and educational domains.
  • Successful cross-lingual alignment achieved for Mongolian-Chinese and Korean-Chinese language pairs.
  • Improved performance in three cross-lingual tasks, addressing data scarcity and structural differences.

Conclusions:

  • CLWKD offers a robust solution for cross-lingual semantic alignment, particularly for morphologically rich languages.
  • The framework effectively leverages knowledge distillation and multi-granularity alignment.
  • Facilitates cost-effective data sharing and improves low-resource language task performance.