arXiv (OAI Expanded)
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Error-Type-Aware Loss Reweighting for Robust Named Entity Recognition with Noisy LLM Labels
Large language models are increasingly used to annotate datasets for training smaller, task-specialized models such as named entity recognition. While this method yields effective models, it assumes that the synthetic dataset is correctly annotated. In this work, we find that (i) current fine-tuning
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