Large Language Models (LLMs) have demonstrated considerable cross-lingual alignment and generalization ability. Current research primarily focuses on improving LLMs' cross-lingual generalization capabilities. However, there is still a lack of research on the intrinsic mechanisms of how LLMs achieve cross-lingual alignment. From the perspective of region partitioning, this paper conducts several investigations on the linguistic competence of LLMs. We discover a core region in LLMs that corresponds to linguistic competence, accounting for approximately 1% of the total model parameters. Removing this core region by setting parameters to zero results in a significant performance decrease across 30 different languages. Furthermore, this core region exhibits significant dimensional dependency, perturbations to even a single parameter on specific dimensions leading to a loss of linguistic competence. Moreover, we discover that distinct monolingual regions exist for different languages, and disruption to these specific regions substantially reduces the LLMs' proficiency in those corresponding languages. Our research also indicates that freezing the core linguistic region during further pre-training can mitigate the issue of catastrophic forgetting (CF), a common phenomenon observed during further pre-training of LLMs. Overall, exploring the LLMs' functional regions provides insights into the foundation of their intelligence.

翻译：大语言模型（LLMs）已展现出显著的跨语言对齐与泛化能力。当前研究主要集中于提升LLMs的跨语言泛化性能。然而，关于LLMs如何实现跨语言对齐的内在机制仍缺乏深入研究。本文从区域划分的视角出发，对LLMs的语言能力进行了多项探究。我们发现LLMs中存在一个与语言能力相对应的核心区域，约占模型总参数的1%。通过将该核心区域的参数置零以移除该区域，会导致模型在30种不同语言上的性能显著下降。此外，该核心区域表现出显著的维度依赖性，即使对特定维度上的单个参数施加扰动，也会导致语言能力的丧失。进一步地，我们发现不同语言存在各自独立的单语区域，干扰这些特定区域会大幅降低LLMs在相应语言上的熟练度。我们的研究还表明，在进一步预训练过程中冻结核心语言区域，可以缓解灾难性遗忘（CF）问题——这是LLMs在进一步预训练中常见的一种现象。总体而言，探索LLMs的功能区域为理解其智能基础提供了新的见解。