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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

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E2MPL: An Enduring and Efficient Meta Prompt Learning Framework for Few-Shot Unsupervised Domain Adaptation.

Wanqi Yang, Haoran Wang, Wei Wang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |December 23, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces Enduring and Efficient Meta-Prompt Learning (E2MPL), a novel framework for few-shot unsupervised domain adaptation (FS-UDA). E2MPL enhances model stability and efficiency, significantly improving accuracy and reducing adaptation time for FS-UDA tasks.

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    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

    983

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Few-shot unsupervised domain adaptation (FS-UDA) aims to improve model performance on unlabeled target domains using limited source domain data.
    • Existing FS-UDA methods struggle with model instability and high time complexity during adaptation to new tasks.

    Purpose of the Study:

    • To propose a novel framework, Enduring and Efficient Meta-Prompt Learning (E2MPL), to address the instability and time-consuming nature of current FS-UDA approaches.
    • To enhance the generalization capabilities and efficiency of models in few-shot unsupervised domain adaptation scenarios.

    Main Methods:

    • Utilizes the pre-trained CLIP model as a backbone for feature learning.
    • Designs domain-shared prompts with virtual tokens to capture meta-knowledge and mitigate domain gaps.
    • Employs a task prompt learning network for adaptive, task-specific prompt generation, enabling fast and stable generalization.
    • Formulates meta-prompt learning as a bilevel optimization problem with a closed-form solution for efficient, single-step adaptation.

    Main Results:

    • Achieved significant improvements on the DomainNet benchmark dataset for FS-UDA.
    • In 5-way 1-shot tasks, E2MPL improved average accuracy by at least 15 percentage points and reduced adaptation time by 64.67%.
    • In 5-way 5-shot tasks, E2MPL improved average accuracy by at least 9 percentage points and reduced adaptation time by 63.18%.
    • Demonstrated enhanced stability, reducing average IQR values by over 40.80% (1-shot) and 25.35% (5-shot).

    Conclusions:

    • E2MPL offers a promising solution for stable and efficient few-shot unsupervised domain adaptation.
    • The framework effectively mitigates domain gaps and achieves superior performance compared to state-of-the-art methods.
    • E2MPL's bilevel optimization and efficient prompt learning enable rapid and robust adaptation to new tasks.