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

arXiv: https://arxiv.org/abs/2505.23689 · HTML: https://mlsanity.com/paper/2505.23689/

## 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 (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [BERT Rediscovers the Classical NLP Pipeline](https://mlsanity.com/paper/1905.05950/) (2019)
- [Deep Learning and the Information Bottleneck Principle](https://mlsanity.com/paper/1503.02406/) (2015)
- [BLiMP: The Benchmark of Linguistic Minimal Pairs for English](https://mlsanity.com/paper/1912.00582/) (2019)
- [Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies](https://mlsanity.com/paper/1611.01368/) (2016)
- [Targeted Syntactic Evaluation of Language Models](https://mlsanity.com/paper/1808.09031/) (2018)
- [What Artificial Neural Networks Can Tell Us About Human Language Acquisition](https://mlsanity.com/paper/2208.07998/) (2022)
- [The emergence of number and syntax units in LSTM language models](https://mlsanity.com/paper/1903.07435/) (2019)
- [What do RNN Language Models Learn about Filler-Gap Dependencies?](https://mlsanity.com/paper/1809.00042/) (2018)
- [Frequency Effects on Syntactic Rule Learning in Transformers](https://mlsanity.com/paper/2109.07020/) (2021)
- [minicons: Enabling Flexible Behavioral and Representational Analyses of Transformer Language Models](https://mlsanity.com/paper/2203.13112/) (2022)
- [Cross-Linguistic Syntactic Evaluation of Word Prediction Models](https://mlsanity.com/paper/2005.00187/) (2020)
- [How to Compute the Probability of a Word](https://mlsanity.com/paper/2406.14561/) (2024)
- [Filtered Corpus Training (FiCT) Shows that Language Models can Generalize from Indirect Evidence](https://mlsanity.com/paper/2405.15750/) (2024)
- [Leading Whitespaces of Language Models' Subword Vocabulary Pose a Confound for Calculating Word Probabilities](https://mlsanity.com/paper/2406.10851/) (2024)
- [A Better Way to Do Masked Language Model Scoring](https://mlsanity.com/paper/2305.10588/) (2023)
- [Is Child-Directed Speech Effective Training Data for Language Models?](https://mlsanity.com/paper/2408.03617/) (2024)
- [How to Plant Trees in Language Models: Data and Architectural Effects on the Emergence of Syntactic Inductive Biases](https://mlsanity.com/paper/2305.19905/) (2023)
- [CLIMB: Curriculum Learning for Infant-inspired Model Building](https://mlsanity.com/paper/2311.08886/) (2023)
- [Evaluating Neural Language Models as Cognitive Models of Language Acquisition](https://mlsanity.com/paper/2310.20093/) (2023)
- [Do Construction Distributions Shape Formal Language Learning In German BabyLMs?](https://mlsanity.com/paper/2503.11593/) (2025)
- [A systematic investigation of learnability from single child linguistic input](https://mlsanity.com/paper/2402.07899/) (2024)
- [Subword models struggle with word learning, but surprisal hides it](https://mlsanity.com/paper/2502.12835/) (2025)
- [Automatic Annotation of Grammaticality in Child-Caregiver Conversations](https://mlsanity.com/paper/2403.14208/) (2024)
- [Learning to Understand Child-directed and Adult-directed Speech](https://mlsanity.com/paper/2005.02721/) (2020)
- [BabyLM Challenge: Exploring the Effect of Variation Sets on Language Model Training Efficiency](https://mlsanity.com/paper/2411.09587/) (2024)
- [Towards Developmentally Plausible Rewards: Communicative Success as a Learning Signal for Interactive Language Models](https://mlsanity.com/paper/2505.05970/) (2025)

## Built on by (most cited first, 9 of 9)

- [Dialogue Is Not Enough to Make a Communicative BabyLM (But Neither Is Developmentally Inspired Reinforcement Learning)](https://mlsanity.com/paper/2510.20358/) (2025)
- [BabyBabelLM: A Multilingual Benchmark of Developmentally Plausible Training Data](https://mlsanity.com/paper/2510.10159/) (2025)
- [Teacher Demonstrations in a BabyLM's Zone of Proximal Development for Contingent Multi-Turn Interaction](https://mlsanity.com/paper/2510.20411/) (2025)
- [A Unified Assessment of the Poverty of the Stimulus Argument for Neural Language Models](https://mlsanity.com/paper/2602.09992/) (2026)
- [Do Syntactic Categories Help in Developmentally Motivated Curriculum Learning for Language Models?](https://mlsanity.com/paper/2511.08199/) (2025)
- [Is Child-Directed Language Optimized for Word Learning? A Computational Study of Verb Meaning Acquisition](https://mlsanity.com/paper/2605.12047/) (2026)
- [Collocational bootstrapping: A hypothesis about the learning of subject-verb agreement in humans and neural networks](https://mlsanity.com/paper/2605.20529/) (2026)
- [Child-directed speech facilitates production, not comprehension, in BabyLMs](https://mlsanity.com/paper/2606.01045/) (2026)
- [A retrospective analysis on the use of LLMs to study infant syntax learning](https://mlsanity.com/paper/2609.26539/) (2026)

## 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.