  Pretrained large language models (LLMs) are widely used in many sub-fields of
natural language processing (NLP) and generally known as excellent few-shot
learners with task-specific exemplars. Notably, chain of thought (CoT)
prompting, a recent technique for eliciting complex multi-step reasoning
through step-by-step answer examples, achieved the state-of-the-art
performances in arithmetics and symbolic reasoning, difficult system-2 tasks
that do not follow the standard scaling laws for LLMs. While these successes
are often attributed to LLMs' ability for few-shot learning, we show that LLMs
are decent zero-shot reasoners by simply adding "Let's think step by step"
before each answer. Experimental results demonstrate that our Zero-shot-CoT,
using the same single prompt template, significantly outperforms zero-shot LLM
performances on diverse benchmark reasoning tasks including arithmetics
(MultiArith, GSM8K, AQUA-RAT, SVAMP), symbolic reasoning (Last Letter, Coin
Flip), and other logical reasoning tasks (Date Understanding, Tracking Shuffled
Objects), without any hand-crafted few-shot examples, e.g. increasing the
accuracy on MultiArith from 17.7% to 78.7% and GSM8K from 10.4% to 40.7% with
large InstructGPT model (text-davinci-002), as well as similar magnitudes of
improvements with another off-the-shelf large model, 540B parameter PaLM. The
versatility of this single prompt across very diverse reasoning tasks hints at
untapped and understudied fundamental zero-shot capabilities of LLMs,
suggesting high-level, multi-task broad cognitive capabilities may be extracted
by simple prompting. We hope our work not only serves as the minimal strongest
zero-shot baseline for the challenging reasoning benchmarks, but also
highlights the importance of carefully exploring and analyzing the enormous
zero-shot knowledge hidden inside LLMs before crafting finetuning datasets or
few-shot exemplars.
