Large Language Models are Zero-Shot Reasoners.pdf

1 Introduction

Scaling up the size of language models has been key ingredients of recent revolutions in natural

language processing (NLP) [Vaswani et al., 2017, Devlin et al., 2019, Raffel et al., 2020, Brown et al.,

2020, Thoppilan et al., 2022, Rae et al., 2021, Chowdhery et al., 2022]. The success of large language

models (LLMs) is often attributed to (in-context) few-shot or zero-shot learning. It can solve various

Few-shot-CoT, Zero-shot-CoT facilitates multi-step reasoning (blue text) and reach correct answer

where standard prompting fails. Unlike Few-shot-CoT using step-by-step reasoning examples

task

all tasks (arithmetic, symbolic, commonsense, and other logical reasoning tasks).

In contrast to the excellent performance of LLMs in intuitive and single-step system-1 [Stanovich

and West, 2000] tasks with task-specific few-shot or zero-shot prompting [Liu et al., 2021b], even

language models at the scale of 100B or more parameters had struggled on system-2 tasks requiring

slow and multi-step reasoning [Rae et al., 2021]. To address this shortcoming, Wei et al. [2022],

Wang et al. [2022] have proposed chain of thought prompting (CoT), which feed LLMs with the

step-by-step reasoning examples rather than standard question and answer examples (see Fig. 1-a).

While the successes of CoT prompting [Wei et al., 2022], along those of many other task-specific

prompting work [Gao et al., 2021, Schick and Schütze, 2021, Liu et al., 2021b], are often attributed

to LLMs’ ability for few-shot learning [Brown et al., 2020], we show that LLMs are decent zero-shot

reasoners by adding a simple prompt, Let’s think step by step, to facilitate step-by-step thinking before

answering each question (see Figure 1). Despite the simplicity, our Zero-shot-CoT successfully

generates a plausible reasoning path in a zero-shot manner and reaches the correct answer in a

problem where the standard zero-shot approach fails. Importantly, our Zero-shot-CoT is versatile and

task-agnostic, unlike most prior task-specific prompt engineering in the forms of examples (few-shot)

or templates (zero-shot) [Liu et al., 2021b]: it can facilitate step-by-step answers across various

reasoning tasks, including arithmetic (MultiArith [Roy and Roth, 2015], GSM8K [Cobbe et al., 2021],

amples, Zero-shot-CoT achieves enormous score gains compared to the zero-shot baseline, e.g. from

model (text-davinci-002). We also evaluate Zero-shot-CoT with another off-the-shelf large model,

Importantly, with our single fixed prompt, zero-shot LLMs have a significantly better scaling curve

comparable to that of the few-shot CoT baseline. We also show that besides Few-shot-CoT requiring

human engineering of multi-step reasoning prompts, their performance deteriorates if prompt example

question types and task question type are unmatched, suggesting high sensitivity to per-task prompt

designs. In contrast, the versatility of this single prompt across diverse reasoning tasks hints at

large language models (LLMs) and prompting, and chain of thought (CoT) prompting for multi-step

reasoning.

Large language models and prompting

the probability distribution over text. Recently, scaling improvements through larger model sizes

billions [Brown et al., 2020] parameters) and larger data (e.g. webtext corpora [Gao et al., 2020])

by modifying the answers in few-shot examples to step-by-step answers, and achieved significant

boosts in performance across these difficult benchmarks, especially when combined with very large

language models like PaLM [Chowdhery et al., 2022]. The top row of Figure 1 shows standard

few-shot prompting against (few-shot) CoT prompting. Notably, few-shot learning was taken as a

given for tackling such difficult tasks, and the zero-shot baseline performances were not even reported

as Few-shot-CoT in this work.

3 Zero-shot Chain of Thought

Few-shot prompting, standard or CoT, avoids needing such answer-extraction prompting by explicitly

designing the few-shot example answers to end in such formats (see the top-right and top-left

For arithmetic reasoning, we consider the following six datasets: (1) SingleEq [Koncel-Kedziorski

For commonsense reasoning, we use CommonsenseQA [Talmor et al., 2019] and StrategyQA [Geva

based on prior knowledge [Talmor et al., 2019]. StrategyQA requires models to infer an implicit

multi-hop reasoning to answer questions [Geva et al., 2021].

4.1 Results

Zero-shot-CoT vs. Zero-shot

four out of six arithmetic reasoning tasks (MultiArith, GSM8K, AQUA, SVAMP), all symbolic

reasoning, and all other logical reasoning tasks (from BIG-bench [Srivastava et al., 2022]). For

our study categorized this task into logical reasoning because this task requires less prior knowledge and more

logical reasoning between dates.

except for No.10-16 in Table 4 in Aug-2022.