当前发育语言模型中语言学习缺失的一半:外源和内源语言输入
Nan Zhao1, Xufeng Duan2,3, Zhenguang G Cai2,3
1Department of Translation, Interpreting and Intercultural Studies, Hong Kong Baptist University, Hong Kong SAR.
Open mind : discoveries in cognitive science
|October 10, 2025
概括
发育性语言模型 (DLM) 可以更好地模仿儿童的语言学习,通过结合内生过程,如内在语言和基于睡眠的学习,以及外部语言输入. 这种整体方法是推动人工智能在语言发展中的关键.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 发展心理学 发展心理学
背景情况:
- 目前的发展性语言模型 (DLM) 主要关注外源语言输入.
- 儿童语言学习受到内源性过程的显著影响,而这些过程在DLM中经常被忽视.
- 内生过程包括非语言感知选择,私人演讲和睡眠期间的神经重播.
研究的目的:
- 为DLM提出一个更全面的框架,考虑到外源和内源语言输入.
- 通过整合儿童语言学习的关键方面,弥合人工语言和人类语言学习之间的差距.
- 重新定义"语言暴露"以包括内部认知和神经机制.
主要方法:
- 整合外源和内源语言输入的概念框架.
- 通过标签反,自我生成的语音和类似睡眠的整合来提升DLM的建议.
- 强调跨学科的合作,以获得经验基础.
主要成果:
- 目前的DLM并没有通过在儿童身上观察到的内生过程来复制语言的放大和精细化.
- 整合内源因素可以显著提高DLM的效率和自然性.
- 重新定义"语言接触"对于开发更类似人类语言学习模型至关重要.
结论:
- DLM需要整合内生过程 (内在语言,睡眠重复,认知选择) 来真正反映孩子的语言学习.
- 未来的DLM应该整合标签反,自我生成的语音和睡眠巩固,以加强学习.
- 跨学科研究对于开发反映人类语言发展复杂性的DLM至关重要.
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