  The use of neural language models to model human behavior has met with mixed
success. While some work has found that the surprisal estimates from these
models can be used to predict a wide range of human neural and behavioral
responses, other work studying more complex syntactic phenomena has found that
these surprisal estimates generate incorrect behavioral predictions. This paper
explores the extent to which the misalignment between empirical and
model-predicted behavior can be minimized by training models on more
developmentally plausible data, such as in the BabyLM Challenge. We trained
teacher language models on the BabyLM "strict-small" dataset and used sentence
level surprisal estimates from these teacher models to create a curriculum. We
found tentative evidence that our curriculum made it easier for models to
acquire linguistic knowledge from the training data: on the subset of tasks in
the BabyLM challenge suite evaluating models' grammatical knowledge of English,
models first trained on the BabyLM data curriculum and then on a few randomly
ordered training epochs performed slightly better than models trained on
randomly ordered epochs alone. This improved linguistic knowledge acquisition
did not result in better alignment with human reading behavior, however: models
trained on the BabyLM dataset (with or without a curriculum) generated
predictions that were as misaligned with human behavior as models trained on
larger less curated datasets. This suggests that training on developmentally
plausible datasets alone is likely insufficient to generate language models
capable of accurately predicting human language processing.
