Brain localization, which describes the association between specific regions of the brain and their corresponding functions, is widely accepted in the field of cognitive science as an objective fact. Today's large language models (LLMs) possess human-level linguistic competence and can execute complex tasks requiring abstract knowledge and reasoning. To deeply understand the inherent mechanisms of intelligence emergence in LLMs, this paper conducts an analogical research using brain localization as a prototype. We have discovered a core region in LLMs that corresponds to linguistic competence, accounting for approximately 1% of the total model parameters. This core region exhibits significant dimension dependency, and perturbations to even a single parameter on specific dimensions can lead to a loss of linguistic competence. Furthermore, we observe that an improvement in linguistic competence does not necessarily accompany an elevation in the model's knowledge level, which might imply the existence of regions of domain knowledge that are dissociated from the linguistic region. Overall, exploring the LLMs' functional regions provides insights into the foundation of their intelligence. In the future, we will continue to investigate knowledge regions within LLMs and the interactions between them.

翻译：大脑定位，即描述大脑特定区域与其对应功能之间的关联，在认知科学领域被广泛视为客观事实。当今的大型语言模型（LLMs）具备人类水平的语言能力，能够执行需要抽象知识和推理的复杂任务。为深入理解LLMs中智能涌现的内在机制，本文以大脑定位为原型开展类比研究。我们发现LLMs中存在一个对应于语言能力的核心区域，其参数约占模型总参数的1%。该核心区域展现出显著的维度依赖性，对特定维度上单个参数的扰动便可能导致语言能力丧失。此外，我们观察到语言能力的提升并不必然伴随模型知识水平的增强，这可能暗示存在与语言区域相分离的领域知识区域。总体而言，探索LLMs的功能区域为其智能基础提供了见解。未来，我们将继续研究LLMs中的知识区域及其相互作用。