English(EN) Error-Type-Aware Loss Reweighting for Robust Named Entity Recognition with Noisy LLM Labels

新方法提高NER模型对含噪声LLM标签的鲁棒性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员开发了一种名为错误类型感知损失重加权的新方法，以提高在大型语言模型（LLM）生成的含噪声数据上训练的命名实体识别（NER）模型的鲁棒性。该方法认识到LLM的标注错误并非均匀分布，不同类型的错误（例如，遗漏提及与类型不正确）对训练信号的影响不同。通过对不同错误类型应用不同的重加权规则，该方法提高了NER性能，在包含24.1%噪声的Wikigold数据集上取得了高达4.6个百分点的提升。 AI

影响增强了在LLM生成数据上训练的模型的可靠性，可能改进下游AI应用。

排序理由学术论文，详细介绍了一种改进机器学习模型训练的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CL TIER_1 English(EN) · Elena Merdjanovska, Jonas Golde, Alan Akbik · 2026-09-01 04:00

面向含噪声LLM标签的鲁棒命名实体识别的错误类型感知损失重加权

arXiv:2608.30827v1 Announce Type: new Abstract: 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 cor…

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面向含噪声LLM标签的鲁棒命名实体识别的错误类型感知损失重加权

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