大型语言模型展示了语言统计学习的潜力

Large Language Models Demonstrate the Potential of Statistical Learning in Language

语言在多大程度上可以仅通过语言输入来习得?这个问题已经困扰了学者们几千年,仍然是语言认知科学中的一个主要争论焦点。人类语言的复杂性阻碍了这一进程,因为语言研究,尤其是涉及计算建模的研究,只能处理人类语言技能的一小部分。我们认为,最新一代的大型语言模型(LLMs)可能最终提供了计算工具,以实证方式确定了人类可以从语言经验中获得多少语言能力。LLMs是基于大量自然语言数据进行训练的复杂深度学习架构,能够执行大量的语言任务。尽管在语义和语用方面存在限制,但我们认为LLMs已经表明,合乎语法的类人语言可以在不需要内置语法的情况下习得。因此,虽然关于人类如何习得和使用语言还有很多要研究的地方,但LLMs为认知科学家提供了成熟的计算模型,可用来以实证方式评估在解释人类语言的全部复杂性方面,统计学习可以带我们走多远。

To what degree can language be acquired from linguistic input alone? This question has vexed scholars for millennia and is still a major focus of debate in the cognitive science of language.The complexity of human language has hampered progress because studies of language-especially those involving computational modeling-have only been able to deal with small fragments of our linguistic skills.We suggest that the most recent generation of Large Language Models(LLMs) might finally provide the computational tools to determine empirically how much of the human language ability can be acquired from linguistic experience.LLMs are sophisticated deep learning architectures trained on vast amounts of natural language data,enabling them to perform an impressive range of linguistic tasks.We argue that,despite their clear semantic and pragmatic limitations,LLMs have already demonstrated that human-like grammatical language can be acquired without the need for a built-in grammar.Thus,while there is still much to learn about how humans acquire and use language,LLMs provide full-fledged computational models for cognitive scientists to empirically evaluate just how far statistical learning might take us in explaining the full complexity of human language.