# A systematic investigation of learnability from single child linguistic input

arXiv: https://arxiv.org/abs/2402.07899 · HTML: https://mlsanity.com/paper/2402.07899/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 12 Feb 2024.

## Builds on (most cited first, 7 of 7)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [What Artificial Neural Networks Can Tell Us About Human Language Acquisition](https://mlsanity.com/paper/2208.07998/) (2022)
- [The MultiBERTs: BERT Reproductions for Robustness Analysis](https://mlsanity.com/paper/2106.16163/) (2021)
- [Language Model Behavior: A Comprehensive Survey](https://mlsanity.com/paper/2303.11504/) (2023)
- [How poor is the stimulus? Evaluating hierarchical generalization in neural networks trained on child-directed speech](https://mlsanity.com/paper/2301.11462/) (2023)
- [Evaluating Neural Language Models as Cognitive Models of Language Acquisition](https://mlsanity.com/paper/2310.20093/) (2023)

## Built on by (most cited first, 8 of 8)

- [Is Child-Directed Speech Effective Training Data for Language Models?](https://mlsanity.com/paper/2408.03617/) (2024)
- [Language Models as Models of Language](https://mlsanity.com/paper/2408.07144/) (2024)
- [The BabyView dataset: High-resolution egocentric videos of infants' and young children's everyday experiences](https://mlsanity.com/paper/2406.10447/) (2024)
- [Child-Directed Language Does Not Consistently Boost Syntax Learning in Language Models](https://mlsanity.com/paper/2505.23689/) (2025)
- [BabyVLM: Data-Efficient Pretraining of VLMs Inspired by Infant Learning](https://mlsanity.com/paper/2504.09426/) (2025)
- [On the robustness of modeling grounded word learning through a child's egocentric input](https://mlsanity.com/paper/2507.14749/) (2025)
- [Do Blind Spots Matter for Word-Referent Mapping? A Computational Study with Infant Egocentric Video](https://mlsanity.com/paper/2511.11725/) (2025)
- [Child-directed speech facilitates production, not comprehension, in BabyLMs](https://mlsanity.com/paper/2606.01045/) (2026)

## Abstract (arXiv)

Language models (LMs) have demonstrated remarkable proficiency in generating linguistically coherent text, sparking discussions about their relevance to understanding human language learnability. However, a significant gap exists between the training data for these models and the linguistic input a child receives. LMs are typically trained on data that is orders of magnitude larger and fundamentally different from child-directed speech (Warstadt and Bowman, 2022; Warstadt et al., 2023; Frank, 2023a). Addressing this discrepancy, our research focuses on training LMs on subsets of a single child's linguistic input. Previously, Wang, Vong, Kim, and Lake (2023) found that LMs trained in this setting can form syntactic and semantic word clusters and develop sensitivity to certain linguistic phenomena, but they only considered LSTMs and simpler neural networks trained from just one single-child dataset. Here, to examine the robustness of learnability from single-child input, we systematically train six different model architectures on five datasets (3 single-child and 2 baselines). We find that the models trained on single-child datasets showed consistent results that matched with previous work, underscoring the robustness of forming meaningful syntactic and semantic representations from a subset of a child's linguistic input.