Unveiling A Core Linguistic Region in Large Language Models
Jun Zhao Zhihao Zhang Yide Ma Qi Zhang Tao Gui Luhui Gao Xuanjing Huang
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.