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Error-Type-Aware Loss Reweighting for Robust Named Entity Recognition with Noisy LLM Labels

arXiv · AI, language, vision and robotics · article · Aug 31, 2026 · UTC

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 processes simply ignore LLM-introduced annotation noise, resulting in degraded performance and (ii) existing noise-robust losses are not transferable to sequence labeling because annotation noise in named entity recognition is heterogeneous: for example, missing mentions and type errors affect the

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