How Do Large Language Models Learn Concepts During Continual Pre-Training?
Human beings primarily understand the world through concepts (e.g., dog), abstract mental representations that structure perception, reasoning, and learning. However, how large language models (LLMs) acquire, retain, and forget such concepts during continual pretraining remains poorly understood. In this work, we study how individual concepts are acquired and forgotten, as well as how multiple concepts interact through interference and synergy. We link these behavioral dynamics to LLMs' internal Concept Circuits, computational subgraphs associated with specific concepts, and incorporate Graph Metrics to characterize circuit structure. Our analysis reveals: (1) LLMs concept circuits provide a non-trivial, statistically significant signal of concept learning and forgetting; (2) Concept circuits exhibit a stage-wise temporal pattern during continual pretraining, with an early increase followed by gradual decrease and stabilization; (3) concepts with larger learning gains tend to exhibit greater forgetting under subsequent training; (4) semantically similar concepts induce stronger interference than weakly related ones; (5) conceptual knowledge differs in their transferability, with some significantly facilitating the learning of others. Together, our findings offer a circuit-level view of concept learning dynamics and inform the design of more interpretable and robust concept-aware training strategies for LLMs.
6966ce63c5e371f6b235cf88
Barry Menglong Yao
claimed_verified
2026-01-14T09:52:33.460Z
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/avatars/fd4f1e8aa0b093c5ab11706d76f6fe5a.svg
Menglong Yao
Barry-Menglong-Yao
user
Barry-Menglong-Yao
6966ce63c5e371f6b235cf89
Sha Li
6966ce63c5e371f6b235cf8a
Yunzhi Yao
claimed_verified
2026-01-23T09:39:07.174Z
6122fbe636a4c36a99dbea7b
/avatars/c0cd2c1ef58e315d9adda9d26000f625.svg
Yunzhi Yao
cowTodd
user
cowTodd
6966ce63c5e371f6b235cf8b
Minqian Liu
6966ce63c5e371f6b235cf8c
Zaishuo Xia
6966ce63c5e371f6b235cf8d
Qifan Wang
6966ce63c5e371f6b235cf8e
Lifu Huang
How Do Large Language Models Learn Concepts During Continual Pre-Training?
Human beings primarily understand the world through concepts (e.g., dog), abstract mental representations that structure perception, reasoning, and learning. However, how large language models (LLMs) acquire, retain, and forget such concepts during continual pretraining remains poorly understood. In this work, we study how individual concepts are acquired and forgotten, as well as how multiple concepts interact through interference and synergy. We link these behavioral dynamics to LLMs' internal Concept Circuits, computational subgraphs associated with specific concepts, and incorporate Graph Metrics to characterize circuit structure. Our analysis reveals: (1) LLMs concept circuits provide a non-trivial, statistically significant signal of concept learning and forgetting; (2) Concept circuits exhibit a stage-wise temporal pattern during continual pretraining, with an early increase followed by gradual decrease and stabilization; (3) concepts with larger learning gains tend to exhibit greater forgetting under subsequent training; (4) semantically similar concepts induce stronger interference than weakly related ones; (5) conceptual knowledge differs in their transferability, with some significantly facilitating the learning of others. Together, our findings offer a circuit-level view of concept learning dynamics and inform the design of more interpretable and robust concept-aware training strategies for LLMs.
Large language models develop concept circuits during continual pretraining that exhibit learning and forgetting patterns, with semantically similar concepts showing stronger interference and varying transferability.
large language models continual pretraining concept circuits semantic similarity interference transferability