Developmentally-plausible Working Memory Shapes a Critical Period for Language Acquisition
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Abstract (arXiv)
Large language models possess general linguistic abilities but acquire language less efficiently than humans. This study proposes a method for integrating the developmental characteristics of working memory during the critical period, a stage when human language acquisition is particularly efficient, into the training process of language models. The proposed method introduces a mechanism that initially constrains working memory during the early stages of training and gradually relaxes this constraint in an exponential manner as learning progresses. Targeted syntactic evaluation shows that the proposed method outperforms conventional methods without memory constraints or with static memory constraints. These findings not only provide new directions for designing data-efficient language models but also offer indirect evidence supporting the role of the developmental characteristics of working memory as the underlying mechanism of the critical period in language acquisition.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 16 of its 56 references that are in the map. First posted on arXiv 7 Feb 2025.
Builds on
16 of 56 references.
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4 more
- 2021CLiMP: A Benchmark for Chinese Language Model Evaluationcited by 44
- 2024Is Child-Directed Speech Effective Training Data for Language Models?cited by 24
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Citing papers per year: 2025: 4; 2026: 6.
10 papers.
- 2025A Review of Incorporating Psychological Theories in LLMscited by 5
- 2025Human-like fleeting memory improves language learning but impairs reading time prediction in transformer language modelscited by 3
- 2025What is the Best Sequence Length for BABYLM?cited by 2
- 2026A Unified Assessment of the Poverty of the Stimulus Argument for Neural Language Modelscited by 1
- 2025Do Self-Supervised Speech Models Exhibit the Critical Period Effects in Language Acquisition?cited by 0
- 2026CxMP: A Linguistic Minimal-Pair Benchmark for Evaluating Constructional Understanding in Language Modelscited by 0
- 2026What Kind of Language is Easy to Language-Model Under Curriculum Learning?cited by 0
- 2026Beyond Static and Linear: What Attention Constraints Best Fit Human Reading Times?cited by 0
- 2026Factors Influencing the Emergence of Dependency Length Minimization in Neural Agent Simulationscited by 0
- 2026Typological Alignment of Stack-Based Language Models on Mildly Context-Sensitive Artificial Languagescited by 0
- 2026Typological Alignment of Stack-Based Language Models on Mildly Context-Sensitive Artificial Languages1 Oct 2026
- 2026Factors Influencing the Emergence of Dependency Length Minimization in Neural Agent Simulations5 Sep 2026
- 2026Beyond Static and Linear: What Attention Constraints Best Fit Human Reading Times?24 Aug 2026
- 2026What Kind of Language is Easy to Language-Model Under Curriculum Learning?29 Apr 2026
- 2026CxMP: A Linguistic Minimal-Pair Benchmark for Evaluating Constructional Understanding in Language Models25 Feb 2026
- 2026A Unified Assessment of the Poverty of the Stimulus Argument for Neural Language Models10 Feb 2026
- 2025What is the Best Sequence Length for BABYLM?22 Oct 2025
- 2025Do Self-Supervised Speech Models Exhibit the Critical Period Effects in Language Acquisition?28 Aug 2025
- 2025Human-like fleeting memory improves language learning but impairs reading time prediction in transformer language models7 Aug 2025
- 2025A Review of Incorporating Psychological Theories in LLMs28 Mar 2025