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Does prompting a large language model (LLM) like GPT-3 with explanationsimprove in-context learning? We study this question on two NLP tasks thatinvolve reasoning over text, namely question answering and natural languageinference. We test the performance of four LLMs on three textual reasoningdatasets 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 tomoderate 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 themodels' predictions nor be factually grounded in the input, even on simpletasks with extractive explanations. However, these flawed explanations canstill be useful as a way to verify LLMs' predictions post-hoc. Through analysisin our three settings, we show that explanations judged by humans to begood--logically consistent with the input and the prediction--more likelycooccur with accurate predictions. Following these observations, we traincalibrators using automatically extracted scores that assess the reliability ofexplanations, allowing us to improve performance post-hoc across all of ourdatasets.

The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning