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A Real-world What-Where-When Memory Test
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走向具有类似人类情节性记忆的大型语言模型
Cody V Dong1, Qihong Lu2, Kenneth A Norman3
1Department of Psychology, Princeton University, Princeton, NJ 08540, USA.
Trends in cognitive sciences
|July 26, 2025
概括
本综述探讨了大型语言模型 (LLM) 如何更好地模拟人类情节性记忆 (EM). 目前的LLM很难预测EM在自然环境中的使用情况,强调需要改进的计算框架.
科学领域:
- 认知神经科学 认知神经科学
- 计算精神病学是一种计算精神病学.
- 人工智能的人工智能
背景情况:
- 插曲性记忆 (EM) 研究已经对现实世界的事件理解有了先进的理解.
- 在计算模型中存在一个空白,用于用复杂的自然数据预测EM使用.
- 机器学习,特别是具有外部内存的大型语言模型 (LLM),提供了潜力,但面临着与人类内存对齐的挑战.
研究的目的:
- 审查当前增强记忆的LLM与人类情节性记忆之间的差异.
- 提出对基准任务的标准,使人工智能模型与人类记忆保持一致.
- 概述用于评估AI记忆模型预测的神经成像方法.
主要方法:
- 认知神经科学和人工智能记忆研究的文献综述.
- 分析当前的LLM架构及其内存增强技术.
- 提出基准任务标准和神经成像评估策略的建议.
主要成果:
- 确定了当前的LLM记忆系统和人类情节性记忆之间的关键差异.
- 建立了开发更好地反映人类记忆的AI模型的标准.
- 建议神经成像方法验证AI记忆预测.
结论:
- 增强记忆的LLM显示出希望,但需要与人类情节性记忆的显著对齐.
- 开发标准化的基准和神经成像验证对于推进人工智能记忆研究至关重要.
- 人工智能和认知神经科学之间的桥梁可以导致更准确的人类记忆的计算模型.
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