Unraveling the cognitive patterns of Large Language Models through
  module communities
Authors: name: Kushal Raj Bhandari, name: Pin-Yu Chen, name: Jianxi Gao

Large Language Models (LLMs) have reshaped our world with significant
advancements in science, engineering, and society through applications ranging
from scientific discoveries and medical diagnostics to Chatbots. Despite their
ubiquity and utility, the underlying mechanisms of LLM remain concealed within
billions of parameters and complex structures, making their inner architecture
and cognitive processes challenging to comprehend. We address this gap by
adopting approaches to understanding emerging cognition in biology and
developing a network-based framework that links cognitive skills, LLM
architectures, and datasets, ushering in a paradigm shift in foundation model
analysis. The skill distribution in the module communities demonstrates that
while LLMs do not strictly parallel the focalized specialization observed in
specific biological systems, they exhibit unique communities of modules whose
emergent skill patterns partially mirror the distributed yet interconnected
cognitive organization seen in avian and small mammalian brains. Our numerical
results highlight a key divergence from biological systems to LLMs, where skill
acquisition benefits substantially from dynamic, cross-regional interactions
and neural plasticity. By integrating cognitive science principles with machine
learning, our framework provides new insights into LLM interpretability and
suggests that effective fine-tuning strategies should leverage distributed
learning dynamics rather than rigid modular interventions.