从语言预测中揭示出现的人类类概念表征
概括
大型语言模型 (LLM) 仅从文本中开发出类似人类的概念. 这些结构化表示反映了人类的认知,并预测了模型的性能,这表明语言预测可以形成概念理解.
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:
- 人类通过经验形成概念.
- 大型语言模型 (LLM) 显示出类似人类的行为,尽管只有文本训练.
- 没有人知道LLM概念表征的性质.
研究的目的:
- 在上下文推断过程中调查LLM概念形成.
- 确定LLM如何表示和组织概念.
- 评估LLM概念与行为之间的关系.
主要方法:
- 法律学士执行了概念推断任务,使用语言描述和上下文线索.
- 分析了LLM内部表示的结构和趋同.
- 与LLM任务执行,人类行为和神经数据相关的表示.
主要成果:
- 劳动法师灵活地从文本和上下文中提取概念.
- 代表性汇聚到一个共享的,独立于背景的结构.
- 与这个结构的调整预测了LLM的表现和人类的判断.
- 法学士代表与人类大脑活动保持一致.
结论:
- 结构化,类似人类的概念表征在LLM中仅仅从语言预测中出现.
- 课程提供了对人类概念结构和智力的洞察力.
- 这些发现支持人工智能与人类智能之间的协调.
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