GoLLIE proposes a model that follows annotation guidelines to enhance zero-shot information extraction. Large Language Models have struggled with Information Extraction tasks due to the complexity of annotation guidelines. GoLLIE outperforms previous attempts by fine-tuning to comply with detailed guidelines. The model leverages pre-training knowledge to extract mentions based on categories defined in the guidelines. However, challenges arise when different annotation schemas define labels differently. The ablation study shows that detailed guidelines are crucial for good results. GoLLIE introduces various training regularizations to ensure compliance with guidelines and prevent overfitting to specific datasets.
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