  How (dis)similar are the learning trajectories of vision-language models and
children? Recent modeling work has attempted to understand the gap between
models' and humans' data efficiency by constructing models trained on less
data, especially multimodal naturalistic data. However, such models are often
evaluated on adult-level benchmarks, with limited breadth in language abilities
tested, and without direct comparison to behavioral data. We introduce
DevBench, a multimodal benchmark comprising seven language evaluation tasks
spanning the domains of lexical, syntactic, and semantic ability, with
behavioral data from both children and adults. We evaluate a set of
vision-language models on these tasks, comparing models and humans not only on
accuracy but on their response patterns. Across tasks, models exhibit variation
in their closeness to human response patterns, and models that perform better
on a task also more closely resemble human behavioral responses. We also
examine the developmental trajectory of OpenCLIP over training, finding that
greater training results in closer approximations to adult response patterns.
DevBench thus provides a benchmark for comparing models to human language
development. These comparisons highlight ways in which model and human language
learning processes diverge, providing insight into entry points for improving
language models.
