  Humans are efficient language learners and inherently social creatures. Our
language development is largely shaped by our social interactions, for example,
the demonstration and feedback from caregivers. Contrary to human language
learning, recent advancements in large language models have primarily adopted a
non-interactive training paradigm, and refined pre-trained models through
feedback afterward. In this work, we aim to examine how corrective feedback
from interactions influences neural language acquisition from the ground up
through systematically controlled experiments, assessing whether it contributes
to learning efficiency in language models. We introduce a
trial-and-demonstration (TnD) learning framework that incorporates three
components: student trials, teacher demonstrations, and a reward conditioned on
language competence at various developmental stages. Our experiments reveal
that the TnD approach accelerates word acquisition for student models of equal
and smaller numbers of parameters, and we highlight the significance of both
trials and demonstrations. We further show that the teacher's choices of words
influence students' word-specific learning efficiency, and a
practice-makes-perfect effect is evident by a strong correlation between the
frequency of words in trials and their respective learning curves. Our findings
suggest that interactive language learning, with teacher demonstrations and
student trials, can facilitate efficient word learning in language models.
