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    Area of Science:

    • Natural Language Processing
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Relation extraction (RE) faces challenges with limited supervised training data.
    • Collecting high-quality, extensive datasets for RE is difficult and resource-intensive.
    • Pretrained language models (PLMs) offer potential for knowledge extraction but require effective prompting strategies.

    Purpose of the Study:

    • To develop a robust few-shot relation extraction (FSRE) method using PLMs.
    • To address the issue of template bias in prompting techniques for FSRE.
    • To enhance the stability and performance of FSRE models in low-data scenarios.

    Main Methods:

    • Eliciting relational and factual knowledge from PLMs via prompting techniques.
    • Automatically generating diverse natural language templates for FSRE.
    • Proposing a template regularization network (TRN) to mitigate template bias.
    • Implementing TRN with attention, ranking regularization, and template calibration mechanisms.

    Main Results:

    • The proposed TRN effectively alleviates template bias, stabilizing FSRE models.
    • Experimental results on FewRel and NYT datasets demonstrate robust superiority over existing methods.
    • The model shows significant improvements in few-shot relation extraction tasks.

    Conclusions:

    • The developed prompting and regularization approach offers a stable and effective solution for few-shot relation extraction.
    • This method successfully leverages PLMs for knowledge elicitation in low-resource settings.
    • The findings suggest a promising direction for advancing RE capabilities with limited data.