自然语法,人工智能和语言习得

Inf. Comput. Pub Date : 2023-07-20 DOI:10.3390/info14070418
W. O'grady, Miseon Lee
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引用次数: 1

摘要

在最近的工作中,许多学者提出,大型语言模型可以解释为语言习得的输入驱动理论。在本文中,我们提出了一种方法来验证这一想法。正如我们将记录的那样,有充分的理由认为,在语言发展的早期阶段,处理压力压倒了输入,创造了一个暂时但复杂的否定系统,而在照顾者的语言中没有对应的系统。我们接下来概述一个(目前)涉及这一现象的思想实验,它可以有助于更深入地理解人类语言和试图模拟它的语言模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Natural Syntax, Artificial Intelligence and Language Acquisition
In recent work, various scholars have suggested that large language models can be construed as input-driven theories of language acquisition. In this paper, we propose a way to test this idea. As we will document, there is good reason to think that processing pressures override input at an early point in linguistic development, creating a temporary but sophisticated system of negation with no counterpart in caregiver speech. We go on to outline a (for now) thought experiment involving this phenomenon that could contribute to a deeper understanding both of human language and of the language models that seek to simulate it.
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