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Updated: Jan 20, 2026

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Performing Behavioral Tasks in Subjects with Intracranial Electrodes
Published on: October 2, 2014
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士在更高层次的思维理论任务上实现了成年人的表现
Winnie Street1, John Oliver Siy1, Geoff Keeling1
1Google, London, United Kingdom.
Frontiers in human neuroscience
|January 19, 2026
概括
大型语言模型 (LLM) 显示了高级思维理论 (ToM) 任务的高级能力,反映了人类对复杂精神状态的推理. GPT-4和Flan-PaLM表现出接近成年人的表现,GPT-4在复杂的推断方面表现出色.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 高阶思维理论 (ToM) 对复杂的社会互动至关重要.
- 之前的研究还没有对高级ToM任务的LLM进行全面评估.
- 在AI中理解ToM对于开发复杂的辅助和社会应用程序至关重要.
研究的目的:
- 评估大型语言模型 (LLM) 执行需要高阶思维理论 (ToM) 的任务的能力.
- 使用新型测试套件将LLM绩效与人类基准进行比较.
- 调查模型大小和培训对ToM能力的影响.
主要方法:
- 开发一个手写的测试套件,心灵的多顺序理论问答.
- 评估了五个不同规模和培训模式的LLM.
- 将LLM成绩与新收集的成年人基准进行比较.
主要成果:
- 在总体的ToM任务中,GPT-4和Flan-PaLM实现了成年级和接近成年级的性能.
- 在第六级推断上,GPT-4的表现超过了成年人的表现.
- 对于更高阶的ToM,观察到模型大小和微调之间的相互作用.
结论:
- 在LLM,特别是GPT-4中,在更高级的ToM推理方面取得了显著进展.
- 法学士的语言能力似乎支持复杂的ToM推理.
- 这些发现对社会,教育和辅助LLM应用有影响.
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