How Do LLMs Acquire New Knowledge? A Knowledge Circuits Perspective on
  Continual Pre-Training
Despite exceptional capabilities in knowledge-intensive tasks, Large Language
Models (LLMs) face a critical gap in understanding how they internalize new
knowledge, particularly how to structurally embed acquired knowledge in their
neural computations. We address this issue through the lens of knowledge
circuit evolution, identifying computational subgraphs that facilitate
knowledge storage and processing. Our systematic analysis of circuit evolution
throughout continual pre-training reveals several key findings: (1) the
acquisition of new knowledge is influenced by its relevance to pre-existing
knowledge; (2) the evolution of knowledge circuits exhibits a distinct phase
shift from formation to optimization; (3) the evolution of knowledge circuits
follows a deep-to-shallow pattern. These insights not only advance our
theoretical understanding of the mechanisms of new knowledge acquisition in
LLMs, but also provide potential implications for improving continual
pre-training strategies to enhance model performance. Code and data will be
available at https://github.com/zjunlp/DynamicKnowledgeCircuits.
67b42223c2fe54b8d43efed6
Yixin Ou
admin_assigned
2025-02-19T15:22:40.840Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/1669210243382-6241749cf80bd930bd99f3dd.jpeg
Ou Yixin
user
OE-Heart
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Yunzhi Yao
admin_assigned
2025-02-19T15:22:56.226Z
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Yunzhi Yao
user
cowTodd
67b42223c2fe54b8d43efed8
Ningyu Zhang
claimed_verified
2025-02-18T09:31:04.227Z
620b3bbb0668e435407c8d0a
/avatars/e0fccbb2577d76088e09f054c35cffbc.svg
Ningyu Zhang
user
Ningyu
67b42223c2fe54b8d43efed9
Hui Jin
67b42223c2fe54b8d43efeda
Jiacheng Sun
admin_assigned
2025-02-19T15:23:08.677Z
6591895250d39af7f431ad3d
/avatars/83c9717e7d8bea8cae6a640ee6455214.svg
Sun Jiacheng
user
sunjc826
67b42223c2fe54b8d43efedb
Shumin Deng
admin_assigned
2025-02-19T15:23:17.528Z
6441f1d2603214724ec0c1c2
/avatars/d3c4b759e6a5635e37ff715fae52e5ba.svg
Shumin Deng
user
231sm
67b42223c2fe54b8d43efedc
Zhenguo Li
67b42223c2fe54b8d43efedd
Huajun Chen
admin_assigned
2025-02-19T15:23:34.297Z
64931296137833d7ec7689cd
https://cdn-avatars.huggingface.co/v1/production/uploads/64931296137833d7ec7689cd/TBihNdp1ZwIWjhfAWjRr6.jpeg
Huajun Chen
user
huajunsir
How Do LLMs Acquire New Knowledge? A Knowledge Circuits Perspective on
  Continual Pre-Training
Despite exceptional capabilities in knowledge-intensive tasks, Large Language
Models (LLMs) face a critical gap in understanding how they internalize new
knowledge, particularly how to structurally embed acquired knowledge in their
neural computations. We address this issue through the lens of knowledge
circuit evolution, identifying computational subgraphs that facilitate
knowledge storage and processing. Our systematic analysis of circuit evolution
throughout continual pre-training reveals several key findings: (1) the
acquisition of new knowledge is influenced by its relevance to pre-existing
knowledge; (2) the evolution of knowledge circuits exhibits a distinct phase
shift from formation to optimization; (3) the evolution of knowledge circuits
follows a deep-to-shallow pattern. These insights not only advance our
theoretical understanding of the mechanisms of new knowledge acquisition in
LLMs, but also provide potential implications for improving continual
pre-training strategies to enhance model performance. Code and data will be
available at https://github.com/zjunlp/DynamicKnowledgeCircuits.
Analysis of knowledge circuit evolution in Large Language Models reveals key patterns in knowledge acquisition and suggests improvements for continual pre-training.
Large Language Models knowledge circuit evolution computational subgraphs knowledge storage knowledge processing continual pre-training phase shift deep-to-shallow pattern