  Large language models (LLMs) demonstrate remarkable multilingual capabilities
without being pre-trained on specially curated multilingual parallel corpora.
It remains a challenging problem to explain the underlying mechanisms by which
LLMs process multilingual texts. In this paper, we delve into the composition
of Transformer architectures in LLMs to pinpoint language-specific regions.
Specially, we propose a novel detection method, language activation probability
entropy (LAPE), to identify language-specific neurons within LLMs. Based on
LAPE, we conduct comprehensive experiments on several representative LLMs, such
as LLaMA-2, BLOOM, and Mistral. Our findings indicate that LLMs' proficiency in
processing a particular language is predominantly due to a small subset of
neurons, primarily situated in the models' top and bottom layers. Furthermore,
we showcase the feasibility to "steer" the output language of LLMs by
selectively activating or deactivating language-specific neurons. Our research
provides important evidence to the understanding and exploration of the
multilingual capabilities of LLMs.
