大型推理模型和人类之间的思维成本是相似的
Andrea Gregor de Varda1, Ferdinando Pio D'Elia2, Hope Kean1
1Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139.
概括
大型神经网络模型
科学领域:
- 认知科学是一种认知科学.
- 人工智能的人工智能是人工智能.
- 神经科学是一个神经科学.
背景情况:
- 人类的推理涉及复杂的认知过程.
- 了解推理的认知需求是一个关键的挑战.
- 神经网络模型越来越多地用于研究认知.
研究的目的:
- 调查神经网络模型是否能够捕捉到人类推理的认知需求.
- 评估模型生成的推理步骤与人类认知努力之间的一致性.
主要方法:
- 评估了七个不同的推理任务.
- 分析了由大型推理模型产生的思维链 (CoT) 长度.
- 与项目和任务级别的人类反应时间相关的CoT长度.
主要成果:
- 模型产生的CoT长度显著预测了人类反应时间.
- 这一预测在单个推理任务 (项目难度) 和不同任务 (整体认知负载) 中都成立.
- 这些发现表明,在捕捉推理复杂性方面,模型与人之间有很强的对齐.
结论:
- 非常规的神经网络推理模型反映了人类认知复杂性的核心特征.
- 这些模型捕捉问题和任务的难度,而不需要明确的象征性推理机制.
- 这项研究为当前大型推理模型的认知可信性提供了证据.
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