Today's large language models (LLMs) routinely generate coherent, grammatical
and seemingly meaningful paragraphs of text. This achievement has led to
speculation that these networks are -- or will soon become -- "thinking
machines", capable of performing tasks that require abstract knowledge and
reasoning. Here, we review the capabilities of LLMs by considering their
performance on two different aspects of language use: 'formal linguistic
competence', which includes knowledge of rules and patterns of a given
language, and 'functional linguistic competence', a host of cognitive abilities
required for language understanding and use in the real world. Drawing on
evidence from cognitive neuroscience, we show that formal competence in humans
relies on specialized language processing mechanisms, whereas functional
competence recruits multiple extralinguistic capacities that comprise human
thought, such as formal reasoning, world knowledge, situation modeling, and
social cognition. In line with this distinction, LLMs show impressive (although
imperfect) performance on tasks requiring formal linguistic competence, but
fail on many tests requiring functional competence. Based on this evidence, we
argue that (1) contemporary LLMs should be taken seriously as models of formal
linguistic skills; (2) models that master real-life language use would need to
incorporate or develop not only a core language module, but also multiple
non-language-specific cognitive capacities required for modeling thought.
Overall, a distinction between formal and functional linguistic competence
helps clarify the discourse surrounding LLMs' potential and provides a path
toward building models that understand and use language in human-like ways.

今天的大型语言模型 (LLMs) 可以生成连贯的，符合语法的、有意义的文本段落，但在如人类思维一样的实际语言使用中，大多数测试需要功能语言能力，从认知神经科学的证据中，我们显示出 LLMs 显示出令人印象深刻（虽然不完美）的正式语言能力的任务，但在需要功能能力的许多测试中失败了。