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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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CSTS: Exploring Class-Specific and Task-Shared Embedding Representation for Few-Shot Learning.

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    This study introduces a novel approach for few-shot learning (FSL) by synchronizing class-specific and task-shared information. The method enhances feature representation for improved object discrimination with limited data.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Few-shot learning (FSL) requires high-quality feature representation from limited labeled data.
    • Existing methods struggle with generalizability due to sample-level or task-level feature extraction limitations.

    Purpose of the Study:

    • To synchronize class-specific and task-shared information for improved FSL feature representation.
    • To overcome limitations of current sample-level and task-level feature extraction in FSL.

    Main Methods:

    • Introduced structure-based contrastive learning to enhance class-specific representations by increasing inter-class distance.
    • Constructed a hierarchical class structure using granular computing for semantic clustering.
    • Developed a hierarchical graph neural network for transferring task-shared information from coarse to fine granularities.

    Main Results:

    • The proposed model effectively synchronizes class-specific and task-shared information.
    • Structure-guided contrastive learning improves the study of class-specific information.
    • Hierarchical graph neural network enables effective transfer of task-shared information.

    Conclusions:

    • The synchronized approach yields a superior feature representation for FSL classification.
    • Experimental results on four benchmark datasets show significant advantages over state-of-the-art models.