  The events in a narrative are understood as a coherent whole via the
underlying states of their participants. Often, these participant states are
not explicitly mentioned, instead left to be inferred by the reader. A model
that understands narratives should likewise infer these implicit states, and
even reason about the impact of changes to these states on the narrative. To
facilitate this goal, we introduce a new crowdsourced English-language,
Participant States dataset, PASTA. This dataset contains inferable participant
states; a counterfactual perturbation to each state; and the changes to the
story that would be necessary if the counterfactual were true. We introduce
three state-based reasoning tasks that test for the ability to infer when a
state is entailed by a story, to revise a story conditioned on a counterfactual
state, and to explain the most likely state change given a revised story.
Experiments show that today's LLMs can reason about states to some degree, but
there is large room for improvement, especially in problems requiring access
and ability to reason with diverse types of knowledge (e.g. physical,
numerical, factual).
