  When reading a story, humans can quickly understand new fictional characters
with a few observations, mainly by drawing analogies to fictional and real
people they already know. This reflects the few-shot and meta-learning essence
of humans' inference of characters' mental states, i.e., theory-of-mind (ToM),
which is largely ignored in existing research. We fill this gap with a novel
NLP dataset, ToM-in-AMC, the first assessment of machines' meta-learning of ToM
in a realistic narrative understanding scenario. Our dataset consists of ~1,000
parsed movie scripts, each corresponding to a few-shot character understanding
task that requires models to mimic humans' ability of fast digesting characters
with a few starting scenes in a new movie.
  We propose a novel ToM prompting approach designed to explicitly assess the
influence of multiple ToM dimensions. It surpasses existing baseline models,
underscoring the significance of modeling multiple ToM dimensions for our task.
Our extensive human study verifies that humans are capable of solving our
problem by inferring characters' mental states based on their previously seen
movies. In comparison, our systems based on either state-of-the-art large
language models (GPT-4) or meta-learning algorithms lags >20% behind,
highlighting a notable limitation in existing approaches' ToM capabilities.
