学习的判断区分了人类与大型语言模型在预测记忆中的区别
Markus Huff1,2, Elanur Ulakci3,4
1Perception and Action Lab, Leibniz-Institut Für Wissensmedien, Schleichstr. 6, 72072, Tübingen, Germany. markus.huff@uni-tuebingen.de.
Scientific reports
|October 7, 2025
概括
大型语言模型 (LLM) 不像人类那样准确地预测记忆性能. 这项研究表明,LLM与元认知斗争,突出了他们对人工智能应用的自我监控能力的差距.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 心理学 心理学 心理学
背景情况:
- 大型语言模型 (LLM) 在语言任务中展示了类似人类的认知能力.
- 超认知,特别是预测自己记忆表现的能力 (学习判断 - JOL),对于有效的学习和自我调节至关重要.
- 在LLMs的元认知能力,特别是他们的JOL准确性,仍然在很大程度上未被探索.
研究的目的:
- 评估当前的LLM (基于ChatGPT) 是否可以准确地预测他们自己的记忆性能,反映人类学习判断 (JOL).
- 调查如何环境适应影响人类和LLMJOL在处理具有挑战性的花园路径句子时.
主要方法:
- 使用跨代理预测模型将人类JOL与LLM JOL进行比较.
- 参与者 (人类和LLM) 被测试了对句子对的记忆回忆,包括花园路径句子.
- 操作了上下文合适 (合适与不合适),以检查其对JOL准确性的影响.
主要成果:
- 人类JOL可靠地预测了实际的记忆性能.
- 没有一个测试的LLM (GPT-3.5-turbo,GPT-4-turbo,GPT-4o) 显示出对记忆性能的准确预测能力.
- 即使在适当的背景下呈现句子时,LLM JOL的准确性也没有得到改善.
结论:
- 在对象级的认知任务中,LLM表现出色,但缺乏元级的自我监测,无法准确地预测记忆性能.
- 这种在LLM的元认知缺陷需要进一步的研究和开发,以提高他们的自我意识.
- 提高LLM的元认知能力对于其有效地融入教育工具和人类-人工智能协作系统至关重要.
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