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The paper "ADELIE" introduces a novel approach to improving Information Extraction (IE) capabilities in Large Language Models through alignment learning. The research presents a framework that enhances LLMs' ability to extract structured information from unstructured text without requiring extensive task-specific training data. The authors demonstrate how reward modeling and reinforcement learning can be used to align LLM outputs with desired IE formats and standards. The framework incorporates self-consistency checking, format validation, and semantic verification to ensure high-quality extractions. Results show significant improvements across various IE tasks, including named entity recognition, relation extraction, and event detection. ADELIE achieves state-of-the-art performance while requiring minimal human supervision, making it particularly valuable for real-world applications where labeled data is scarce. The paper also addresses challenges in maintaining extraction accuracy while ensuring output consistency and format compliance.
