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English(EN) Enhancing Biomedical Named Entity Recognition via Multiple Programming Languages Instruction Tuning and Ensemble Method

新的MITE方法使用编程语言改进BioNER

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了MITE，一种通过利用多种编程语言进行指令调优来改进生物医学命名实体识别（BioNER）的新颖方法。该方法将BioNER任务重新表述为Python、C++和Java等语言的代码格式表示，提供了多样化的结构化监督，而无需外部知识。在推理过程中，MITE聚合来自不同代码格式的预测，以增强鲁棒性和准确性。在六个BioNER数据集上的实验表明，MITE的性能持续优于现有的基于BERT和基于LLM的方法，并展现出强大的跨数据集泛化能力。 AI

影响 该方法可以提高生物医学信息提取系统的准确性和鲁棒性。

排序理由 该集群包含一篇详细介绍特定NLP任务新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的MITE方法使用编程语言改进BioNER

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该集群包含一篇详细介绍特定NLP任务新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Songtao Li, Yijia Zhang, Jianyuan Yuan, Shidi Zhang, Fengyu Zhang, Hongfei Lin ·

    通过多编程语言指令微调和集成方法增强生物医学命名实体识别

    arXiv:2610.02949v1 Announce Type: new Abstract: Instruction tuning has become a common paradigm for applying large language models (LLMs) to biomedical named entity recognition (BioNER). However, existing instruction-tuning approaches still face two key challenges. First, convent…

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