@inproceedings{padovani-etal-2025-child,
title = "Child-Directed Language Does Not Consistently Boost Syntax Learning in Language Models",
author = "Padovani, Francesca and
Jumelet, Jaap and
Matusevych, Yevgen and
Bisazza, Arianna",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.999/",
doi = "10.18653/v1/2025.emnlp-main.999",
pages = "19735--19756",
ISBN = "979-8-89176-332-6",
abstract = "Seminal work by Huebner et al. (2021) showed that language models (LMs) trained on English Child-Directed Language (CDL) can outperform LMs trained on an equal amount of adult-directed text like Wikipedia. However, it remains unclear whether these results generalize across languages, architectures, and evaluation settings. We test this by comparing models trained on CDL vs. Wikipedia across two LM objectives (masked and causal), three languages (English, French, German), and three syntactic minimal pair benchmarks. Our results on these benchmarks show inconsistent benefits of CDL, which in most cases is outperformed by Wikipedia models. We then identify various shortcomings in these benchmarks, and introduce a novel testing methodology, FIT-CLAMS, which uses a frequency-controlled design to enable balanced comparisons across training corpora. Through minimal pair evaluations and regression analysis we show that training on CDL does not yield stronger generalizations for acquiring syntax and highlight the importance of controlling for frequency effects when evaluating syntactic ability."
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<abstract>Seminal work by Huebner et al. (2021) showed that language models (LMs) trained on English Child-Directed Language (CDL) can outperform LMs trained on an equal amount of adult-directed text like Wikipedia. However, it remains unclear whether these results generalize across languages, architectures, and evaluation settings. We test this by comparing models trained on CDL vs. Wikipedia across two LM objectives (masked and causal), three languages (English, French, German), and three syntactic minimal pair benchmarks. Our results on these benchmarks show inconsistent benefits of CDL, which in most cases is outperformed by Wikipedia models. We then identify various shortcomings in these benchmarks, and introduce a novel testing methodology, FIT-CLAMS, which uses a frequency-controlled design to enable balanced comparisons across training corpora. Through minimal pair evaluations and regression analysis we show that training on CDL does not yield stronger generalizations for acquiring syntax and highlight the importance of controlling for frequency effects when evaluating syntactic ability.</abstract>
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%0 Conference Proceedings
%T Child-Directed Language Does Not Consistently Boost Syntax Learning in Language Models
%A Padovani, Francesca
%A Jumelet, Jaap
%A Matusevych, Yevgen
%A Bisazza, Arianna
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F padovani-etal-2025-child
%X Seminal work by Huebner et al. (2021) showed that language models (LMs) trained on English Child-Directed Language (CDL) can outperform LMs trained on an equal amount of adult-directed text like Wikipedia. However, it remains unclear whether these results generalize across languages, architectures, and evaluation settings. We test this by comparing models trained on CDL vs. Wikipedia across two LM objectives (masked and causal), three languages (English, French, German), and three syntactic minimal pair benchmarks. Our results on these benchmarks show inconsistent benefits of CDL, which in most cases is outperformed by Wikipedia models. We then identify various shortcomings in these benchmarks, and introduce a novel testing methodology, FIT-CLAMS, which uses a frequency-controlled design to enable balanced comparisons across training corpora. Through minimal pair evaluations and regression analysis we show that training on CDL does not yield stronger generalizations for acquiring syntax and highlight the importance of controlling for frequency effects when evaluating syntactic ability.
%R 10.18653/v1/2025.emnlp-main.999
%U https://aclanthology.org/2025.emnlp-main.999/
%U https://doi.org/10.18653/v1/2025.emnlp-main.999
%P 19735-19756
Markdown (Informal)
[Child-Directed Language Does Not Consistently Boost Syntax Learning in Language Models](https://aclanthology.org/2025.emnlp-main.999/) (Padovani et al., EMNLP 2025)
ACL