Self-Improving for Zero-Shot Named Entity Recognition with Large Language Models (2024.naacl-short)
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| Challenge: | Existing studies exploring the performance of large language models on named entity recognition tasks have focused on training task-specific LLMs for NER. |
| Approach: | They propose a training-free self-improving framework that utilizes an unlabeled corpus to stimulate the self-learning ability of LLMs. |
| Outcome: | The proposed framework improves performance on the named entity recognition task by using an unlabeled corpus. |
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