关于生成人工智能和生成语言学的兼容性
Eva Portelance1,2, Masoud Jasbi3
1Department of Decision Sciences, HEC, Montréal, Québec, Canada. eva.portelance@hec.ca.
Nature computational science
|September 16, 2025
概括
生成型人工智能 (AI) 与乔姆斯基的生成语言学保持一致,提供了评估和改进神经语言模型的方法. 这项研究将象征性人工智能和现代人工智能联系起来,以了解人类语言和认知.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 语言学的语言学.
背景情况:
- 乔姆斯基开创的生成语言学显著影响了认知科学和象征性AI.
- 神经语言模型的出现引发了关于它们与生成语言学原则的兼容性的辩论.
研究的目的:
- 探索生成人工智能 (AI) 和生成语言学之间的对齐.
- 展示生成AI如何支持生成语言学的核心概念.
- 建立生成语言学作为评估和增强神经语言模型的框架.
主要方法:
- 生成性语言学核心思想的概念分析.
- 确定生成AI和生成语言学之间的对齐点.
- 建议生成语言学作为神经语言模型的评估框架.
主要成果:
- 确定了生成人工智能和生成语言学之间的三个关键调整领域.
- 生成性人工智能证明了对生成性语言学的基本原则的支持.
- 生成性语言学为评估神经语言模型的认知和语言有效性提供了一个强大的框架.
结论:
- 生成型人工智能和生成型语言学共享基本原则,促进相互支持.
- 生成性语言学为推进神经语言模型提供了必要的标准.
- 这种协同作用通过人工智能增强了我们对人类语言和认知的理解.
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