arXiv cs.CL· Forough Shirin Abkenar, Haoshuo Fu, Liang Feng, Ning Yan, Masood S. Mortazavi·· 3 天前AI 评分25
研究追踪 LLM 语言习得中的形态、语义与分歧
Tracing Morphology, Semantics, and Divergence in Language Acquisition
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研究利用 Pythia 和 OLMo 的中间检查点，在 teacher-forcing 评估下追踪 LLM 跨训练步与模型规模的语言习得过程。结果显示模型习得形态结构远早于语义嵌入稳定，训练数据集规模对分歧识别有正向影响，而预训练中的共现与推理时的重复则产生负面影响。
正文 · 原文
Abstract:Understanding the dynamics of language acquisition in Large Language Models (LLMs) is crucial for uncovering how statistical representations evolve into structured linguistic capabilities. However, most existing studies focus primarily on fully trained models, leaving the developmental trajectory across intermediate training stages largely underexplored. In this work, we leverage intermediate checkpoints from open-source models (e.g., Pythia and OLMo) under teacher-forcing evaluation to systematically investigate language acquisition across training steps and model scales. We focus on four core characteristics: morphological structure acquisition, embedding drift, repetition dominance, and co-occurrence effects. Our findings reveal that models acquire morphological structures well before semantic embeddings stabilize. Furthermore, we show that training dataset size has a positive impact on divergence recognition, while co-occurrence during pre-training and repetition during inference have a negative impact on divergence recognition and associative link generation.
| Subjects: | Machine Learning (cs.LG); Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.19041 [cs.LG] |
| (or arXiv:2610.19041v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.19041 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Forough Shirin Abkenar [view email]
[v1]
Tue, 13 Oct 2026 18:29:52 UTC (21,804 KB)
来源：arXiv cs.CL · arxiv.org