Large language models (LLMs) have come closest among all models to date to mastering human language, yet opinions about their linguistic and cognitive capabilities remain split. Here, we evaluate LLMs using a distinction between formal linguistic competence (knowledge of linguistic rules and patterns) and functional linguistic competence (understanding and using language in the world). We ground this distinction in human neuroscience, which has shown that formal and functional competence rely on different neural mechanisms. Although LLMs are surprisingly good at formal competence, their performance on functional competence tasks remains spotty and often requires specialized fine-tuning and/or coupling with external modules. We posit that models that use language in human-like ways would need to master both of these competence types, which, in turn, could require the emergence of separate mechanisms specialized for formal versus functional linguistic competence.

中文翻译：

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在大型语言模型中分离语言和思想


迄今为止，大型语言模型（LLMs）是所有模型中最接近掌握人类语言的模型，但对其语言和认知能力的看法仍然存在分歧。在这里，我们使用形式语言能力（语言规则和模式的知识）和功能语言能力（理解和使用世界上的语言）之间的区别来评估LLMs。我们将这种区别建立在人类神经科学的基础上，该科学表明形式能力和功能能力依赖于不同的神经机制。尽管 LLMs 在形式能力方面出人意料地出色，但它们在功能能力任务上的表现仍然参差不齐，并且通常需要专门的微调和/或与外部模块的耦合。我们假设以类似人类的方式使用语言的模型需要掌握这两种能力类型，这反过来可能需要出现专门用于形式语言能力和功能语言能力的单独机制。