  Large language models (LLMs) have achieved widespread success on a variety of
in-context few-shot tasks, but this success is typically evaluated via
correctness rather than consistency. We argue that self-consistency is an
important criteria for valid multi-step reasoning in tasks where the solution
is composed of the answers to multiple sub-steps. We propose two types of
self-consistency that are particularly important for multi-step reasoning --
hypothetical consistency (a model's ability to predict what its output would be
in a hypothetical other context) and compositional consistency (consistency of
a model's final outputs when intermediate sub-steps are replaced with the
model's outputs for those steps). We demonstrate that multiple variants of the
GPT-3/-4 models exhibit poor consistency rates across both types of consistency
on a variety of tasks.
