This is the repository of the paper Is a Large Language Model a Good Annotator for Event Extraction.
we introduce an innovative approach that leverages Large Language Models (LLMs) for enhancing Event Extraction, which employ LLMs as expert annotators.
In doing so, we strategically include sample data from the training dataset in the prompt as a reference, ensuring alignment between the data distribution of LLM generated samples and the benchmark dataset, thereby addressing the challenges of data imbalance and scarcity, while simultaneously enhancing the performance of fine-tuned EE-specific models.
The methodology we present not only improves the performance of event extraction but also demonstrates a novel approach to alleviate the longstanding issues of data scarcity and imbalance.
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Augmented ACE2005 dataset (GPT-3.5 turbo, PaLM, GPT4)
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Augmented MAVEN dataset (GPT-3.5 turbo, PaLM)
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Appendix of the paper