Papers with feedback and synthesis history

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Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation Extraction (2024.emnlp-main)

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Challenge: Existing Relation extraction models require extensive annotated training data, which is costly and labor-intensive to collect.
Approach: They propose a new zero-shot RE task where only relation definitions are provided instead of seen-unseen relation instances.
Outcome: The proposed task significantly improves cost-effective zero-shot performance by large margins.

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