建模记忆,预测前景:使用大型语言模型对自传细节叙述的自动评分
Jonas Klus1,2, Daniel E Cohen3, Alexis N Garcia3
1Department of Psychology, University of Arizona, Tucson, AZ, USA. jklus@arizona.edu.
Behavior research methods
|August 1, 2025
概括
本研究引入了一种使用大型语言模型 (LLM) 来分析自传记忆叙事的自动评分模型. 该法学士准确地区分内部和外部细节,为记忆研究提供了可扩展的替代手动评分的替代方案.
科学领域:
- 认知心理学 认知心理学
- 人工智能的人工智能
- 神经科学是一个神经科学.
背景情况:
- 自传采访对于记忆和认知功能评估至关重要.
- 区分内部 (情节性) 和外部 (语义) 细节是关键.
- 手动评分这些细节是耗时和资源密集的.
研究的目的:
- 开发和验证自传记忆和未来思考任务的自动评分模型.
- 利用大型语言模型 (LLM) 来分析个人事件叙述.
- 为大规模的记忆研究提供可扩展的解决方案.
主要方法:
- 在个人事件叙述上微调了LLaMA-3大语言模型 (LLM).
- 在三项研究中对284名参与者的数据进行了训练和测试.
- 评估模型性能与内部和外部细节的人类得分对比.
主要成果:
- 自动化模型与人类得分有很强的相关性 (r=0.87用于内部,r=0.84用于外部细节).
- 该模型复制了已知的与年龄相关的记忆趋势,即使在人类评估者没有这样做时,也识别了差异.
- 该LLM表现出与人类评级者高度一致,与评级者之间的可靠性相当.
结论:
- 基于LLM的自动评分为分析自传记忆提供了一种可行和可扩展的方法.
- 这种方法提高了大规模认知和记忆研究的效率.
- 研究人员可以使用开发的模型和附带的Jupyter笔记本.
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