大型语言模型中的事件细分应用程序启用了自动回忆评估.
Ryan A Panela1,2, Alexander J Barnett3,4, Morgan D Barense5,3
1Rotman Research Institute, Baycrest Academy for Research and Education, North York, Toronto, ON, Canada. ryan.panela@utoronto.ca.
Communications psychology
|December 15, 2025
概括
大型语言模型 (LLM) 现在可以在叙述中自动化事件细分和回忆评估. 这种人工智能方法为记忆和感知研究提供了一个可扩展和准确的替代主观人类评估.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 神经科学是一个神经科学.
背景情况:
- 事件细分对于感知,编码和记忆回忆至关重要,影响理解和体验.
- 目前评估事件细分和回忆的方法依赖于主观,耗时的人类判断.
- 现有的自动化方法缺乏足够的有效性和易于实施.
研究的目的:
- 利用大型语言模型 (LLM) 来自动化事件细分和回忆评估在书面叙述中.
- 为了验证基于LLM的方法与人类注释的准确性和一致性.
- 开发一个可扩展的AI驱动的框架来研究感知和记忆.
主要方法:
- 利用聊天完成模型从叙述中实现自动事件细分.
- 采用文本嵌入模型来评估细分叙事事件的回忆.
- 根据人类细分模式和回忆性能验证的LLM输出.
主要成果:
- 在叙述中,LLM准确地识别了事件边界,证明了其高有效性.
- 人类事件细分显示,与LLM输出相比,与人类注释者之间的一致性更大.
- 使用语义相似性的自动回忆评估有效估计了参与者的回忆性能.
结论:
- LLM提供了一个可扩展和准确的替代方案,用于事件细分和回忆手动评分.
- 这种人工智能驱动的方法提高了对感知,记忆和认知障碍的研究.
- 这些发现为人工智能辅助的认知研究开辟了新的途径.
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