  Does prompting a large language model (LLM) like GPT-3 with explanations
improve in-context learning? We study this question on two NLP tasks that
involve reasoning over text, namely question answering and natural language
inference. We test the performance of four LLMs on three textual reasoning
datasets using prompts that include explanations in multiple different styles.
For these tasks, we find that including explanations in the prompts for OPT,
GPT-3 (davinci), and InstructGPT (text-davinci-001) only yields small to
moderate accuracy improvements over standard few-show learning. However,
text-davinci-002 is able to benefit more substantially.
  We further show that explanations generated by the LLMs may not entail the
models' predictions nor be factually grounded in the input, even on simple
tasks with extractive explanations. However, these flawed explanations can
still be useful as a way to verify LLMs' predictions post-hoc. Through analysis
in our three settings, we show that explanations judged by humans to be
good--logically consistent with the input and the prediction--more likely
cooccur with accurate predictions. Following these observations, we train
calibrators using automatically extracted scores that assess the reliability of
explanations, allowing us to improve performance post-hoc across all of our
datasets.
