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How Do LLMs Acquire New Knowledge? A Knowledge Circuits Perspective on Continual Pre-Training

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arxiv 2502.11196 v2 pith:W5OVOHSU submitted 2025-02-16 cs.LG cs.AIcs.CLcs.CVcs.HC

Yixin Ou , Yunzhi Yao , Ningyu Zhang , Hui Jin , Jiacheng Sun , Shumin Deng , Zhenguo Li , Huajun Chen This is my paper
classification cs.LGcs.AIcs.CLcs.CVcs.HC
keywords knowledgeevolutioncircuitscontinualllmspre-trainingacquisitioncircuit
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 4 Pith papers

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  1. Benchmarking Knowledge Editing using Logical Rules

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    Introduces a benchmark using logical rules from knowledge graphs to generate multi-hop questions that evaluate whether knowledge edits in LLMs propagate to entailed facts, finding up to 24% performance gaps for method...

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  3. Crosscoding Through Time: Tracking Emergence & Consolidation Of Linguistic Representations Throughout LLM Pretraining

    cs.CL 2025-09 unverdicted novelty 6.0 of 10

    Sparse crosscoders on LLM checkpoint triplets track emergence, maintenance, and discontinuation of linguistic features during pretraining via a new RelIE metric.

  4. Progressive Homeostatic and Plastic Prompt Tuning for Audio-Visual Multi-Task Incremental Learning

    cs.AI 2025-07 reject novelty 4.0 of 10

    A three-stage prompt-tuning method for audio-visual multi-task incremental learning is proposed, reporting state-of-the-art results on AVE, AVVP, AVS, and AVQA, with caveats about its evaluation metric and ablations.

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