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Computer Science > Computation and Language

arXiv:2509.19371 (cs)
[Submitted on 19 Sep 2025]

Title:How to inject knowledge efficiently? Knowledge Infusion Scaling
Law for Pre-training Large Language Models

Authors:Kangtao Lv, Haibin Chen, Yujin Yuan, Langming Liu, Shilei Liu
, Yongwei Wang, Wenbo Su, Bo Zheng
View a PDF of the paper titled How to inject knowledge efficiently?
Knowledge Infusion Scaling Law for Pre-training Large Language
Models, by Kangtao Lv and 7 other authors
View PDF HTML (experimental)

    Abstract:Large language models (LLMs) have attracted significant
    attention due to their impressive general capabilities across
    diverse downstream tasks. However, without domain-specific
    optimization, they often underperform on specialized knowledge
    benchmarks and even produce hallucination. Recent studies show
    that strategically infusing domain knowledge during pretraining
    can substantially improve downstream performance. A critical
    challenge lies in balancing this infusion trade-off: injecting
    too little domain-specific data yields insufficient
    specialization, whereas excessive infusion triggers catastrophic
    forgetting of previously acquired knowledge. In this work, we
    focus on the phenomenon of memory collapse induced by
    over-infusion. Through systematic experiments, we make two key
    observations, i.e. 1) Critical collapse point: each model
    exhibits a threshold beyond which its knowledge retention
    capabilities sharply degrade. 2) Scale correlation: these
    collapse points scale consistently with the model's size.
    Building on these insights, we propose a knowledge infusion
    scaling law that predicts the optimal amount of domain knowledge
    to inject into large LLMs by analyzing their smaller
    counterparts. Extensive experiments across different model sizes
    and pertaining token budgets validate both the effectiveness and
    generalizability of our scaling law.

Subjects: Computation and Language (cs.CL); Artificial Intelligence
          (cs.AI)
Cite as:  arXiv:2509.19371 [cs.CL]
          (or arXiv:2509.19371v1 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2509.19371
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Kangtao Lv [view email]
[v1] Fri, 19 Sep 2025 07:46:10 UTC (311 KB)
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    Large Language Models, by Kangtao Lv and 7 other authors
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