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脑电睡眠分期实验把大模型用于清理训练标签

两名专家面对同一段脑电记录，有时会给出不同的睡眠分期。研究者把这种标注分歧当成训练数据问题来处理。团队先用原始标注训练TinySleepNet，再挑出分类器把握较低的片段，将脑电频谱特征和前后片段一起交给LLaMA 3.1。大模型依据睡眠评分规范标记可疑样本，团队移除这些样本后，重新训练原来的轻量模型。 Elmira Salari和Shruti Kshirsagar的这项研究收录在11月6日更新的计算机科学预印本列表中。

在Sleep-EDF Expanded数据集的20折交叉验证中，准确率由81.8%升至84.6%，兼顾各类样本的宏平均F1分数由75.6%升至77.4%，快速眼动睡眠的识别改善较明显。大模型只在训练时参与判断，实际运行的分类器仍约有130万个参数。这一结果来自公开数据集验证，尚须和佩戴设备在日常环境中的实测表现分开看待。

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