大型语言和人工智能建模的应用,用于预测同行评审结果
Benjamin S Hopkins1, Ishan Shah1, Jonathan Dallas1
11Department of Neurological Surgery, Keck School of Medicine of the University of Southern California, Los Angeles, California.
Journal of neurosurgery
|January 22, 2026
概括
微调的人工智能 (AI) 模型,特别是GPT-3,可以使用评论员评论预测学术手稿的接受. 这个AI应用程序显示了简化同行评审过程的潜力.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 学术出版学术出版公司
背景情况:
- 人工智能 (AI) 提供了增强学术期刊同行评审的机会.
- 大型语言模型 (LLM) 可以分析文本数据以获得洞察力.
研究的目的:
- 通过同行评审者的评论和编辑决定来微调LLM.
- 为了识别基于文本的预测者对手稿的接受与拒绝.
主要方法:
- 分析了2021-2023年匿名审稿人的评论和决定.
- 包括BERT,GPT-3和GPT-4o在内的LLM进行了微调和评估.
- 使用接收器操作特征 (ROC) 曲线和SHAP分析来评估性能.
主要成果:
- 微调的GPT-3和GPT-4mini在预测手稿决策方面实现了0.91的最高AUC.
- SHAP分析确定了与接受 (例如"未来"",有趣") 和拒绝 (例如"不清楚") 相关的关键词.
结论:
- 精心调整的AI模型,特别是GPT-3,可以从评论员的评论中高准确地预测手稿的接受.
- 文章的清晰度,实用性和作者对查询的响应能力是影响结果的关键主题.
- 人工智能具有很大的潜力,可以帮助和改进学术同行评审过程.
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