A systematic investigation of learnability from single child linguistic
  input
Yulu Qin Wentao Wang Brenden M. Lake
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.