  Language models are achieving impressive performance on various tasks by
aggressively adopting inference-time prompting techniques, such as zero-shot
and few-shot prompting. In this work, we introduce EchoPrompt, a simple yet
effective approach that prompts the model to rephrase its queries before
answering them. EchoPrompt is adapted for both zero-shot and few-shot
in-context learning with standard and chain-of-thought prompting. Experimental
results show that EchoPrompt yields substantial improvements across all these
settings for four families of causal language models. These improvements are
observed across various numerical reasoning (e.g. GSM8K, SVAMP), reading
comprehension (e.g. DROP), and logical reasoning (e.g. Coin Flipping) tasks. On
average, EchoPrompt improves the Zero-shot-CoT performance of code-davinci-002
by 5% in numerical tasks and 13% in reading comprehension tasks. We investigate
the factors contributing to EchoPrompt's effectiveness through ablation
studies, which reveal that both the original query and the model-generated
rephrased version are instrumental in its performance gains. Our empirical
results indicate that EchoPrompt is an effective technique that enhances
in-context learning performance. We recommend incorporating EchoPrompt into
various baseline prompting strategies to achieve performance boosts.
