  Language models (LMs) often generate incoherent outputs: they refer to events
and entity states that are incompatible with the state of the world described
in their inputs. We introduce SituationSupervision, a family of approaches for
improving coherence in LMs by training them to construct and condition on
explicit representations of entities and their states. SituationSupervision has
two components: an auxiliary situation modeling task that trains models to
predict state representations in context, and a latent state inference
procedure that imputes these states from partially annotated training data.
SituationSupervision can be applied to both fine-tuning (by supervising LMs to
encode state variables in their hidden representations) and prompting (by
inducing LMs to interleave textual descriptions of entity states with output
text). In both cases, SituationSupervision requires only a small number of
state annotations to produce major coherence improvements (between 4-11%),
showing that standard LMs can be sample-efficiently trained to model not just
language but the situations it describes.
