mlsanityData to 14 Oct 2026

Child-Directed Language Does Not Consistently Boost Syntax Learning in Language Models

arXiv 2505.23689 ↗First posted 29 May 2025cs.CLcited by 9

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Abstract (arXiv)

Seminal work by Huebner et al. (2021) showed that language models (LMs) trained on English Child-Directed Language (CDL) can reach similar syntactic abilities as LMs trained on much larger amounts of adult-directed written text, suggesting that CDL could provide more effective LM training material than the commonly used internet-crawled data. However, the generalizability of these results across languages, model types, and evaluation settings remains unclear. 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 previous 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.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 29 of its 59 references that are in the map. First posted on arXiv 29 May 2025.

Builds on

29 of 59 references.

References · 29

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  4. 2019BERT Rediscovers the Classical NLP Pipelinecited by 1,059
  5. 2015Deep Learning and the Information Bottleneck Principlecited by 802
  6. 2019BLiMP: The Benchmark of Linguistic Minimal Pairs for Englishcited by 564
  7. 2016Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependenciescited by 354
  8. 2018Targeted Syntactic Evaluation of Language Modelscited by 242
  9. 2022What Artificial Neural Networks Can Tell Us About Human Language Acquisitioncited by 99
  10. 2019The emergence of number and syntax units in LSTM language modelscited by 94
  11. 2018What do RNN Language Models Learn about Filler-Gap Dependencies?cited by 86
  12. 2021Frequency Effects on Syntactic Rule Learning in Transformerscited by 59
17 more
  1. 2022minicons: Enabling Flexible Behavioral and Representational Analyses of Transformer Language Modelscited by 58
  2. 2020Cross-Linguistic Syntactic Evaluation of Word Prediction Modelscited by 55
  3. 2024How to Compute the Probability of a Wordcited by 36
  4. 2024Filtered Corpus Training (FiCT) Shows that Language Models can Generalize from Indirect Evidencecited by 35
  5. 2024Leading Whitespaces of Language Models' Subword Vocabulary Pose a Confound for Calculating Word Probabilitiescited by 30
  6. 2023A Better Way to Do Masked Language Model Scoringcited by 27
  7. 2024Is Child-Directed Speech Effective Training Data for Language Models?cited by 24
  8. 2023How to Plant Trees in Language Models: Data and Architectural Effects on the Emergence of Syntactic Inductive Biasescited by 23
  9. 2023CLIMB: Curriculum Learning for Infant-inspired Model Buildingcited by 20
  10. 2023Evaluating Neural Language Models as Cognitive Models of Language Acquisitioncited by 15
  11. 2025Do Construction Distributions Shape Formal Language Learning In German BabyLMs?cited by 11
  12. 2024A systematic investigation of learnability from single child linguistic inputcited by 8
  13. 2025Subword models struggle with word learning, but surprisal hides itcited by 6
  14. 2024Automatic Annotation of Grammaticality in Child-Caregiver Conversationscited by 5
  15. 2020Learning to Understand Child-directed and Adult-directed Speechcited by 4
  16. 2024BabyLM Challenge: Exploring the Effect of Variation Sets on Language Model Training Efficiencycited by 4
  17. 2025Towards Developmentally Plausible Rewards: Communicative Success as a Learning Signal for Interactive Language Modelscited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 5.

9 papers.

  1. 2025Dialogue Is Not Enough to Make a Communicative BabyLM (But Neither Is Developmentally Inspired Reinforcement Learning)cited by 11
  2. 2025BabyBabelLM: A Multilingual Benchmark of Developmentally Plausible Training Datacited by 10
  3. 2025Teacher Demonstrations in a BabyLM's Zone of Proximal Development for Contingent Multi-Turn Interactioncited by 9
  4. 2026A Unified Assessment of the Poverty of the Stimulus Argument for Neural Language Modelscited by 1
  5. 2025Do Syntactic Categories Help in Developmentally Motivated Curriculum Learning for Language Models?cited by 0
  6. 2026Is Child-Directed Language Optimized for Word Learning? A Computational Study of Verb Meaning Acquisitioncited by 0
  7. 2026Collocational bootstrapping: A hypothesis about the learning of subject-verb agreement in humans and neural networkscited by 0
  8. 2026Child-directed speech facilitates production, not comprehension, in BabyLMscited by 0
  9. 2026A retrospective analysis on the use of LLMs to study infant syntax learningcited by 0