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Can Child Language Acquisition be Modeled by Linear Causal or Bidirectional Masked Models Trained on Child-Directed Input?: A Comparison of Baby Language Models and Children in Acquiring Wanna…

Children aged 3 to 7 were sensitive to the wanna-contraction constraint and showed age-related development, but none of the 30 baby language models trained on child-directed input up to ages 3 through 7 reproduced that pattern, according to a study by Haerim Hwang published in the Proceedings of the Second BabyLM Workshop (pages 50–64, Budapest, Hungary, Association for Computational Linguistics). The study compared an autoregressive causal LM represented by GPT and a bidirectional masked LM represented by RoBERTa, testing wanna contraction, a phenomenon not previously tested in language modeling. Hwang concluded the baby LMs do not fully capture human-like development, suggesting the learning mechanisms behind child language acquisition do not entirely align with the tested architectures and cannot be reduced to input-driven learning alone within these models.

read2 min views2 publishedOct 24, 2026
Can Child Language Acquisition be Modeled by Linear Causal or Bidirectional Masked Models Trained on Child-Directed Input?: A Comparison of Baby Language Models and Children in Acquiring Wanna…
Image: Aclanthology (auto-discovered)
Abstract

Children’s remarkable ability to acquire language remains one of the most fascinating questions in cognitive science. How do they develop sophisticated syntactic knowledge? To contribute to this question, the present study investigates the role of input and learning mechanisms by modeling child language acquisition across increasing age. Specifically, this study trains 30 baby language models (LMs) on naturalistic input directed to children up to ages 3 through 7 and compares their syntactic knowledge directly with that of children aged 3 to 7, focusing on the constraint on wanna contraction, a phenomenon not previously tested in language modeling. Furthermore, the study examines two major LM architectures: an autoregressive causal LM, represented by GPT, and a bidirectional masked LM, represented by RoBERTa. The results showed that children were sensitive to the constraint and displayed age-related development, but neither GPT-based nor RoBERTa-based baby models showed the same pattern. In this respect, the baby LMs do not seem to fully capture human-like development, possibly suggesting that the learning mechanisms underlying child language acquisition do not entirely align with the architectures tested here and cannot be reduced to input-driven learning alone within the context of these models.

- Anthology ID:
- 2026.babylm-main.5
- Volume:
- [Proceedings of the Second BabyLM Workshop](https://aclanthology.org/volumes/2026.babylm-main/)
- Month:
- Venues:
- [BabyLM](https://aclanthology.org/venues/babylm/) |[WS](https://aclanthology.org/venues/ws/)
- SIG:
- Publisher:
  • Association for Computational Linguistics
- Note:
- Pages:
  • 50–64
- Language:
- URL:
- [https://aclanthology.org/2026.babylm-main.5/](https://aclanthology.org/2026.babylm-main.5/)
- DOI:
- Cite (ACL):
- PDF:
- [https://aclanthology.org/2026.babylm-main.5.pdf](https://aclanthology.org/2026.babylm-main.5.pdf)
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