  Recent research has shown that rationales, or step-by-step chains of thought,
can be used to improve performance in multi-step reasoning tasks. We reconsider
rationale-augmented prompting for few-shot in-context learning, where (input ->
output) prompts are expanded to (input, rationale -> output) prompts. For
rationale-augmented prompting we demonstrate how existing approaches, which
rely on manual prompt engineering, are subject to sub-optimal rationales that
may harm performance. To mitigate this brittleness, we propose a unified
framework of rationale-augmented ensembles, where we identify rationale
sampling in the output space as the key component to robustly improve
performance. This framework is general and can easily be extended to common
natural language processing tasks, even those that do not traditionally
leverage intermediate steps, such as question answering, word sense
disambiguation, and sentiment analysis. We demonstrate that rationale-augmented
ensembles achieve more accurate and interpretable results than existing
prompting approaches--including standard prompting without rationales and
rationale-based chain-of-thought prompting--while simultaneously improving
interpretability of model predictions through the associated rationales.
