大型语言模型预测认知和教育接近或比基因组学或专家评估更好
1Faculty of Sociology, Bielefeld University, Bielefeld, Germany. tobias.wolfram@uni-bielefeld.de.
Communications psychology
|July 3, 2025
概括
分析文章的大型语言模型可以预测认知和非认知特征,以及教育成就,优于传统方法. 将文本分析与遗传学和教师数据相结合,进一步提高了对这些关键发育结果的预测准确性.
科学领域:
- 行为科学 行为科学
- 计算语言学 计算语言学
- 遗传学 遗传学 是一个
背景情况:
- 传统的社会调查在预测教育和心理结果方面具有有限的力量.
- 预测发展中的个体差异仍然是行为科学中的一个关键挑战.
研究的目的:
- 探索教育成就,认知能力和非认知特征的可预测性,使用新的计算方法.
- 评估大型语言模型和机器学习在预测心理社会特征方面的有效性.
主要方法:
- 使用了独特的纵向数据集,并训练了SuperLearner框架对短期望论文 (11岁) 的训练.
- 综合计算语言学措施和基于大型语言模型 (LLM) 的嵌入.
- 开发了一种组合模型,将文本数据,遗传标记和教师评估结合起来.
主要成果:
- 使用LLM嵌入式准确预测认知和非认知特征,可与教师评估相提并优于基因组数据.
- 实现了最终教育成绩的高可预测性,优于基因组数据.
- 集合模型预测了接近测试重试可靠性的认知能力 (RHoldout2 = 0.7),并解释了38%的成就差异.
结论:
- 在LLMs和机器学习的进步为行为科学家提供了强大的新工具.
- 基于文本的论文分析为发展结果提供了显著的预测能力.
- 整合多个数据源 (文本,遗传学,教师评估) 可以最大限度地提高预测准确性.
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