Original: Large Language Models are Zero-Shot Reasoners
Revised: Large Language Models are Zero-Shot Reasoners

Original: Anonymous Author(s)
Revised: Anonymous Author(s)

Original: AffiliationAddress email
Revised: AffiliationAddress email

Original: Abstract
Revised: Abstract

Original: Pretrained large language models (LLMs) are widely used in many sub-fields ofnatural language processing (NLP) and generally known as excellent few-shotlearners with task-specific exemplars.
Revised: Pretrained large language models (LLMs) are widely used in many sub-fields ofnatural language processing (NLP) and generally known as excellent few-shotlearners with task-specific exemplars.

Original: Notably, chain of thought (CoT) prompting,a recent technique for eliciting complex multi-step reasoning through step-bystep answer examples, achieved the state-of-the-art performances in arithmeticsand symbolic reasoning, difficult system-2 tasks that do not follow the standardscaling laws for LLMs.
Revised: Notably, chain of thought (CoT) prompting,a recent technique for eliciting complex multi-step reasoning through step-bystep answer examples, achieved the state-of-the-art performances in arithmeticsand symbolic reasoning, difficult system-2 tasks that do not follow the standardscaling laws for LLMs.

Original: While these successes are often attributed to LLMs’ability for few-shot learning, we show that LLMs are decent zero-shot reasonersby simply adding “Let’s think step by step” before each answer.
Revised: While these successes are often attributed to LLMs’ability for few-shot learning, we show that LLMs are decent zero-shot reasonersby simply adding “Let’s think step by step” before each answer.

Original: Experimentalresults demonstrate that our Zero-shot-CoT, using the same single prompt template,significantly outperforms zero-shot LLM performances on diverse benchmarkreasoning 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 fewshot examples, e.g. increasing the accuracy on MultiArith from 17.7% to 78.7%and GSM8K from 10.4% to 40.7% withan off-the-shelf 175B parameter model.
Revised: Experimentalresults demonstrate that our Zero-shot-CoT, using the same single prompt template,significantly outperforms zero-shot LLM performances on diverse benchmarkreasoning 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 fewshot examples, e.g. increasing the accuracy on MultiArith from 17.7% to 78.7%and GSM8K from 10.4% to 40.7% with 175B parameter InstructGPT model,as well as similar magnitudes of improvements with another off-the-shelf largemodel, 540B parameter PaLM.

Original: The versatility of this single prompt across very diverse reasoning tasks hints atuntapped and understudied fundamental zero-shot capabilities of LLMs, suggestinghigh-level, multi-task broad cognitive capabilities may be extracted through simpleprompting.
Revised: The versatility of this single prompt across verydiverse reasoning tasks hints at untapped and understudied fundamental zero-shotcapabilities of LLMs, suggesting high-level, multi-task broad cognitive capabilitiesmay be extracted by simple prompting.

Original: We hope our work not only serves as the minimal strongest zeroshot baseline for the challenging reasoning benchmarks, but also highlights theimportance of carefully exploring and analyzing the enormous zero-shot knowledgehidden inside LLMs before crafting finetuning datasets or few-shot exemplars.
Revised: We hope our work not only serves as theminimal strongest zero-shot baseline for the challenging reasoning benchmarks, butalso highlights the importance of carefully exploring and analyzing the enormouszero-shot knowledge hidden inside LLMs before crafting finetuning datasets orfew-shot exemplars.

Original:  Introduction
Revised:  Introduction

Original: Scaling up the size of language models has been key ingredients of recent revolutions in naturallanguage processing (NLP)
Revised: Scaling up the size of language models has been key ingredients of recent revolutions in naturallanguage processing (NLP)

Original: [Vaswani et al., 2017, Devlin et al., 2019, Raffel et al., 2020, Brown et al.
Revised: [Vaswani et al., 2017, Devlin et al., 2019, Raffel et al., 2020, Brown et al.

Original: ,2020, Thoppilan et al., 2022, Rae et al., 2021, Chowdhery et al., 2022].
Revised: ,2020, Thoppilan et al., 2022, Rae et al., 2021, Chowdhery et al., 2022].

Original: The success of large languagemodels (LLMs) is often attributed to (in-context) few-shot or zero-shot learning.
Revised: The success of large languagemodels (LLMs) is often attributed to (in-context) few-shot or zero-shot learning.

Original: It can solve varioustasks by simply conditioning the models on a few examples (few-shot) or instructions describing thetask (zero-shot).
Revised: It can solve varioustasks by simply conditioning the models on a few examples (few-shot) or instructions describing thetask (zero-shot).

Original: The method of conditioning the language model is called “prompting” [Liu et al.,2021b], and designing prompts either manually [Schick and Schütze, 2021, Reynolds and McDonell,2021] or automatically [Gao et al., 2021, Shin et al., 2020] becomes a hot topic in NLP.
Revised: The method of conditioning the language model is called “prompting” [Liu et al.,2021b], and designing prompts either manually [Schick and Schütze, 2021, Reynolds and McDonell,2021] or automatically [Gao et al., 2021, Shin et al., 2020] has become a hot topic in NLP.

Original: In contrast to the excellent performance of LLMs in intuitive and single-step system-1 [Stanovichand West, 2000] tasks with task-specific few-shot or zero-shot prompting [Liu et al., 2021b], evenlanguage models at the scale of 100B or more parameters had struggled on system-2 tasks requiringslow and multi-step reasoning [Rae et al., 2021].
Revised: In contrast to the excellent performance of LLMs in intuitive and single-step system-1 [Stanovichand West, 2000] tasks with task-specific few-shot or zero-shot prompting [Liu et al., 2021b], evenlanguage models at the scale of 100B or more parameters had struggled on system-2 tasks requiringslow and multi-step reasoning [Rae et al., 2021].

Original: To address this shortcoming, Wei et al.
Revised: To address this shortcoming, Wei et al.

Revised: [Figure]

Revised: shot-CoT).

Revised: Similar to

Revised: Few-shot-CoT, Zero-shot-CoT facilitates multi-step reasoning (blue text) and reach correct answer where standard prompting fails.

Revised: Unlike Few-shot-CoT using step-by-step reasoning examples per task , ours does not need any examples and just uses the same prompt “Let’s think step by step” across all tasks (arithmetic, symbolic, commonsense, and other logical reasoning tasks).

Original: Wang et al.
Revised: Wang et al.

Original: [2022] have proposed chain of thought prompting (CoT), which feed LLMs with thestep-by-step reasoning examples rather than standard question and answer examples (see Fig.
Revised: [2022] have proposed chain of thought prompting (CoT), which feed LLMs with thestep-by-step reasoning examples rather than standard question and answer examples (see Fig.

Original: 1-a).
Revised: 1-a).

Original: where standard prompting fails.

Original: Unlike Few-shot-CoT using step-by-step reasoning examples per task , ours does not need any examples and just uses the same prompt “Let’s think step by step” across all tasks (arithmetic, symbolic, commonsense, and other logical reasoning tasks).

Original: Such chain of thought demonstrations facilitate models to generate a reasoning path that decomposesthe complex reasoning into multiple easier steps.
Revised: Such chain of thought demonstrations facilitate models to generate a reasoning path that decomposesthe complex reasoning into multiple easier steps.

Original: Notably with CoT, the reasoning performance thensatisfies the scaling laws better and jumps up with the size of the language models.
Revised: Notably with CoT, the reasoning performance thensatisfies the scaling laws better and jumps up with the size of the language models.

Original: For example,when combined with the 540B parameter PaLM model [Chowdhery et al., 2022], chain of thoughtprompting significantly increases the performance over standard few-shot prompting across severalbenchmark reasoning tasks, e.g., GSM8K (17.9% → 58.1%).
Revised: For example,when combined with the 540B parameter PaLM model [Chowdhery et al., 2022], chain of thoughtprompting significantly increases the performance over standard few-shot prompting across severalbenchmark reasoning tasks, e.g., GSM8K (17.9% → 58.1%).

Original: While the successes of CoT prompting [Wei et al., 2022], along those of many other task-specificprompting work [Gao et al., 2021, Schick and Schütze, 2021, Liu et al., 2021b], are often attributedto LLMs’ ability for few-shot learning [Brown et al., 2020], we show that LLMs are decent zero-shotreasoners by adding a simple prompt, Let’s think step by step , to facilitate step-by-step thinking beforeanswering each question (see Figure 1).
Revised: While the successes of CoT prompting [Wei et al., 2022], along those of many other task-specificprompting work [Gao et al., 2021, Schick and Schütze, 2021, Liu et al., 2021b], are often attributedto LLMs’ ability for few-shot learning [Brown et al., 2020], we show that LLMs are decent zero-shotreasoners by adding a simple prompt, Let’s think step by step , to facilitate step-by-step thinking beforeanswering each question (see Figure 1).

Original: Despite the simplicity, our Zero-shot-CoT successfullygenerates a plausible reasoning path in a zero-shot manner and reaches the correct answer in aproblem where the standard zero-shot approach fails.
Revised: Despite the simplicity, our Zero-shot-CoT successfullygenerates a plausible reasoning path in a zero-shot manner and reaches the correct answer in aproblem where the standard zero-shot approach fails.

Original: Importantly, our Zero-shot-CoT is versatile andtask-agnostic , unlike most prior task-specific prompt engineering in the forms of examples (few-shot)or templates (zero-shot)
Revised: Importantly, our Zero-shot-CoT is versatile andtask-agnostic , unlike most prior task-specific prompt engineering in the forms of examples (few-shot)or templates (zero-shot)

Original: [Liu et al., 2021b]: it can facilitate step-by-step answers across variousreasoning tasks, including arithmetic (MultiArith [Roy and Roth, 2015], GSM8K [Cobbe et al., 2021],
Revised: [Liu et al., 2021b]: it can facilitate step-by-step answers across variousreasoning tasks, including arithmetic (MultiArith [Roy and Roth, 2015], GSM8K [Cobbe et al., 2021],

Original: AQUA-RAT
Revised: AQUA-RAT

Original: [Ling et al., 2017], and SVAMP [Patel et al., 2021]), symbolic (Last letter and Coinflip), commonsense reasoning (CommonSenseQA [Talmor et al., 2019] and Strategy QA [Geva et al.,2021]), and other logical reasoning tasks (Date understanding and Tracking Shuffled Objects from
Revised: [Ling et al., 2017], and SVAMP [Patel et al., 2021]), symbolic (Last letter and Coinflip), commonsense reasoning (CommonSenseQA [Talmor et al., 2019] and Strategy QA [Geva et al.,2021]), and other logical reasoning tasks (Date understanding and Tracking Shuffled Objects from

Original: BIG-bench [big, 2021]) without modifying the prompt per task.
Revised: BIG-bench [big, 2021]) without modifying the prompt per task.

Original: We empirically evaluate Zero-shot-CoT against other prompting baselines in Figure 1.
Revised: We empirically evaluate Zero-shot-CoT against other prompting baselines in Table 2.

Original: While our
Revised: While our

Original: Zero-shot-CoT underperforms Few-shot-CoT with carefully-crafted and task-specific step-by-stepexamples, Zero-shot-CoT achieves enormous score gains compared to the zero-shot baseline, e.g.from 17.7% to 78.7% on MultiArith and from 10.4% to 40.7% on  GSM8K. Importantly, with oursingle fixed prompt, zero-shot LLMs have a significantly better scaling curve comparable to that ofthe few-shot CoT baseline.
Revised: Zero-shot-CoT underperforms Few-shot-CoT with carefully-crafted and task-specific step-by-stepexamples, Zero-shot-CoT achieves enormous score gains compared to the zero-shot baseline, e.g.from 17.7% to 78.7% on MultiArith and from 10.4% to 40.7% on GSM8K with 175B parameter InstructGPT model. We also evaluate Zero-shot-CoT with another off-the-shelf large model, 540Bparameter PaLM, showing similar magnitudes of improvements on MultiArith and GSM8K. Importantly, with our single fixed prompt, zero-shot LLMs have a significantly better scaling curvecomparable to that of the few-shot CoT baseline.

Original: We also show that besides Few-shot-CoT requiring human engineering ofmulti-step reasoning prompts, their performance deteriorates if prompt example question types andtask question type are unmatched, suggesting high sensitivity to per-task prompt designs.
Revised: We also show that besides Few-shot-CoT requiring human engineering of multi-step reasoning prompts, their performance deteriorates if prompt examplequestion types and task question type are unmatched, suggesting high sensitivity to per-task promptdesigns.

Original: In contrast,the versatility of this single prompt across diverse reasoning tasks hints at untapped and understudiedzero-shot fundamental capabilities of LLMs, such as higher-level broad cognitive capabilities like generic logical reasoning [Chollet, 2019].
Revised: In contrast, the versatility of this single prompt across diverse reasoning tasks hints atuntapped and understudied zero-shot fundamental capabilities of LLMs, such as higher-level broadcognitive capabilities like generic logical reasoning [Chollet, 2019].

Original: While the vibrant field of LLMs started out from thepremise of excellent few-shot learners [Brown et al., 2020], we hope our work encourages moreresearch into uncovering high-level and multi-task zero-shot capabilities hidden inside those models.
Revised: While the vibrant field of LLMsstarted out from the premise of excellent few-shot learners [Brown et al., 2020], we hope our workencourages more research into uncovering high-level and multi-task zero-shot capabilities hiddeninside those models.

Original:  Background
Revised:  Background

Original: We briefly review the two core preliminary concepts that form the basis of this work: the advent oflarge language models (LLMs) and prompting, and chain of thought (CoT) prompting for multi-stepreasoning.
Revised: We briefly review the two core preliminary concepts that form the basis of this work: the advent oflarge language models (LLMs) and prompting, and chain of thought (CoT) prompting for multi-stepreasoning.

Original: Large language models and prompting A language model (LM), is a model that looks to estimatethe probability distribution over text.
Revised: Large language models and prompting A language model (LM), is a model that looks to estimatethe probability distribution over text.

Original: Recently, scaling improvements through larger model sizes(from a few million [Merity et al., 2016] to hundreds of millions [Devlin et al., 2019] to hundreds ofbillions [Brown et al., 2020] parameters) and larger data (e.g. webtext corpora [Gao et al., 2020])have enabled pre-trained large language models (LLMs) to be incredibly adept at many downstream
Revised: Recently, scaling improvements through larger model sizes(from a few million [Merity et al., 2016] to hundreds of millions [Devlin et al., 2019] to hundreds ofbillions [Brown et al., 2020] parameters) and larger data (e.g. webtext corpora [Gao et al., 2020])have enabled pre-trained large language models (LLMs) to be incredibly adept at many downstream

Original: NLP tasks.
Revised: NLP tasks.

Original: Besides the classic “pre-train and fine-tune” paradigm [Liu et al., 2021b], models scaledto 100B+ parameters exhibit properties conducive to few-shot learning [Brown et al., 2020], by wayof in context learning, where one can use a text or template known as a prompt to strongly guide thegeneration to output answers for desired tasks, thus beginning an era of “pre-train and prompt” [Liuet al., 2021a].
Revised: Besides the classic “pre-train and fine-tune” paradigm [Liu et al., 2021b], models scaledto 100B+ parameters exhibit properties conducive to few-shot learning [Brown et al., 2020], by wayof in context learning, where one can use a text or template known as a prompt to strongly guide thegeneration to output answers for desired tasks, thus beginning an era of “pre-train and prompt” [Liuet al., 2021a].

Original: In work, we call such prompts with explicit conditioning on few task examples asfew-shot prompts, and other template-only prompts as zero-shot prompts.
Revised: In work, we call such prompts with explicit conditioning on few task examples asfew-shot prompts, and other template-only prompts as zero-shot prompts.

Original: Chain of thought prompting Multi-step arithmetic and logical reasoning benchmarks have particularly challenged the scaling laws of large language models [Rae et al., 2021].
Revised: Chain of thought prompting Multi-step arithmetic and logical reasoning benchmarks have particularly challenged the scaling laws of large language models [Rae et al., 2021].

Original: Chain of thought(CoT) prompting [Wei et al., 2022], an instance of few-shot prompting, proposed a simple solutionby modifying the answers in few-shot examples to step-by-step answers, and achieved significantboosts in performance across these difficult benchmarks, especially when combined with very largelanguage models like PaLM [Chowdhery et al., 2022].
Revised: Chain of thought(CoT) prompting [Wei et al., 2022], an instance of few-shot prompting, proposed a simple solutionby modifying the answers in few-shot examples to step-by-step answers, and achieved significantboosts in performance across these difficult benchmarks, especially when combined with very largelanguage models like PaLM [Chowdhery et al., 2022].

Original: The top row of Figure 1 shows standardfew-shot prompting against (few-shot) CoT prompting.
Revised: The top row of Figure 1 shows standardfew-shot prompting against (few-shot) CoT prompting.

Original: Notably, few-shot learning was taken as agiven for tackling such difficult tasks, and the zero-shot baseline performances were not even reportedin the original work [Wei et al., 2022].
Revised: Notably, few-shot learning was taken as agiven for tackling such difficult tasks, and the zero-shot baseline performances were not even reportedin the original work [Wei et al., 2022].

Original: To differentiate it from our method, we call Wei et al.
Revised: To differentiate it from our method, we call Wei et al.

Original: [2022]as Few-shot-CoT in this work.
Revised: [2022]as Few-shot-CoT in this work.

Original:  Zero-shot Chain of Thought
Revised:  Zero-shot Chain of Thought

Original: We propose Zero-shot-CoT, a zero-shot template-based prompting for chain of thought reasoning.
Revised: We propose Zero-shot-CoT, a zero-shot template-based prompting for chain of thought reasoning.

Original: It differs from the original chain of thought prompting [Wei et al., 2022] as it does not requirestep-by-step few-shot examples, and it differs from most of the prior template prompting [Liu et al.
Revised: It differs from the original chain of thought prompting [Wei et al., 2022] as it does not requirestep-by-step few-shot examples, and it differs from most of the prior template prompting [Liu et al.

Original: ,2021b] as it is inherently task-agnostic and elicits multi-hop reasoning across a wide range of taskswith a single template.
Revised: ,2021b] as it is inherently task-agnostic and elicits multi-hop reasoning across a wide range of taskswith a single template.

Original: The core idea of our method is simple, as described in Figure 1: add Let’sthink step by step , or a a similar text (see Table 5), to extract step-by-step reasoning.
Revised: The core idea of our method is simple, as described in Figure 1: add Let’sthink step by step , or a a similar text (see Table 4), to extract step-by-step reasoning.

Original: 3.1 Two-stage prompting
Revised: 3.1 Two-stage prompting

Original: While Zero-shot-CoT is conceptually simple,its subtlety is that it uses prompting twice, as explainedin Figure 2.
Revised: While Zero-shot-CoT is conceptually simple, it uses prompting twice to extract both reasoning andanswer, as explained in Figure 2.

Original: This is due to the fact that the zero-shot baseline (see the bottom-left in Figure 1) alreadyuses prompting in the form of “The answer is”, to extract the answers in correct formats.
Revised: In contrast, the zero-shot baseline (see the bottom-left in Figure 1)already uses prompting in the form of “The answer is”, to extract the answers in correct formats.

Original: Few-shotprompting, standard or CoT, avoids needing such answer-extraction prompting by explicitly designingthe few-shot example answers to end in such formats (see the top-right in Figure 1).In summary, Few-shot-CoT
Revised: Few-shot prompting, standard or CoT, avoids needing such answer-extraction prompting by explicitlydesigning the few-shot example answers to end in such formats (see the top-right and top-leftin Figure 1).

Original:  [Wei et al., 2022] requires careful human engineering of a few prompt examples withspecific answer formats per task, while Zero-shot-CoT does not require such engineering but requiresprompting LLMs twice.1st prompt: reasoning extraction
Revised: In summary, Few-shot-CoT [Wei et al., 2022] requires careful human engineering ofa few prompt examples with specific answer formats per task, while Zero-shot-CoT requires less engineering but requires prompting LLMs twice [Figure] [Table] [Figure]

Revised: [Table]

Revised: [Figure]

Original: In this step we first modify the input question x into a promptusing a simple template “Q: [X] .
Revised: [Table] [Figure] [Table] [Figure]

Revised: [Table]

Revised: [Figure]

Revised: [Table]

Revised: [Figure]

Revised: [Table]

Revised: [Figure]

Revised: [Table]

Revised: [Figure]

Revised: [Table]

Original: A: [Z] ”, where [X] is an input slot for x and [T] is an slot forhand-crafted trigger sentence t that would extract chain of though to answer the question x .
Revised: [Figure] [Table] [Figure] [Table]

Original: Forexample, if we use “Let’s think step by step” as a trigger sentence, the prompt would be “Q: [X] . A:
Revised: [Figure] [Table]

Original: [Figure]
Revised: [Figure]

Original: [Table]

Original: [Figure]

Original: [Table]

Original: [Figure] [Table] [Figure] [Table]
Revised: 1st prompt: reasoning extraction

Original: [Figure]

Original: [Table] [Figure] [Table] [Figure]
Revised: In this step we first modify the input question x into a promptusing a simple template “Q: [X] .

Original: [Table]

Original: [Figure] [Table] [Figure] [Table]
Revised: A: [T] ”, where [X] is an input slot for x and [T] is an slot forhand-crafted trigger sentence t that would extract chain of though to answer the question x .

Original: [Figure]

Original: [Table] [Figure] [Table] [Figure]
Revised: Forexample, if we use “Let’s think step by step” as a trigger sentence, the prompt would be “Q: [X] .

Original: [Table] [Figure] [Table] [Figure]
Revised: A:

Original: Let’s think step by step.”.
Revised: Let’s think step by step.”.

Original: Prompted text x ′ is then fed into a language model and generate subsequentsentence z .
Revised: Prompted text x ′ is then fed into a language model and generate subsequentsentence z .

Original: We can use any decoding strategy, but we used greedy decoding throughout the paper forthe simplicity.2nd prompt: answer extraction In the second step, we use generated sentence z along withprompted sentence x ′ to extract the final answer from the language model.
Revised: We can use any decoding strategy, but we used greedy decoding throughout the paper forthe simplicity.2nd prompt: answer extraction In the second step, we use generated sentence z along withprompted sentence x ′ to extract the final answer from the language model.

Original: To be concrete, wesimply concatenate three elements as with “ [X ′ ]
Revised: To be concrete, wesimply concatenate three elements as with “ [X ′ ]

Original: [Z] [A] ”:
Revised: [Z] [A] ”:

Original: [X ′ ] for 1st prompt x ′ , [Z] for sentencegenerated at the first step, and [A] for a trigger sentence to extract answer.
Revised: [X ′ ] for 1st prompt x ′ , [Z] for sentencegenerated at the first step, and [A] for a trigger sentence to extract answer.

Original: The prompt for this step isself-augmented , since the prompt contains the sentence z generated by the same language model.
Revised: The prompt for this stepis self-augmented , since the prompt contains the sentence z generated by the same language model.

Original: Inexperiment, we use slightly different answer trigger depends on the answer format.
Revised: In experiment, we use slightly different answer trigger depending on the answer format.

Original: For example, weuse “Therefore, among A through E, the answer is” for multi-choice QA, and “Therefore, the answer(arabic numerals) is” for math problem requiring numerical answer.
Revised: For example,we use “Therefore, among A through E, the answer is” for multi-choice QA, and “Therefore, theanswer (arabic numerals) is” for math problem requiring numerical answer.

Original: See Appendix A.5 for the details.
Revised: See Appendix A.5 forthe details.

Original: Finally, the language model is fed the prompted text as input to generate sentences ˆy and parse thefinal answer.
Revised: Finally, the language model is fed the prompted text as input to generate sentences ˆy andparse the final answer.

Original:  Experiment
Revised:  Experiment

Original: Tasks and datasets We evaluate our proposal on 12 datasets from four categories of reasoningtasks: arithmetic, commonsense, symbolic, and other logical reasoning tasks.
Revised: Tasks and datasets We evaluate our proposal on 12 datasets from four categories of reasoningtasks: arithmetic, commonsense, symbolic, and other logical reasoning tasks.

Original: See Appendix A.2 forthe detailed description of each datasets.
Revised: See Appendix A.2 forthe detailed description of each datasets.

Original: For arithmetic reasoning, we consider the following six datasets: (1) SingleEq [Koncel-Kedziorskiet al., 2015], (2) AddSub [Hosseini et al., 2014], (3)
Revised: For arithmetic reasoning, we consider the following six datasets: (1) SingleEq [Koncel-Kedziorskiet al., 2015], (2) AddSub [Hosseini et al., 2014], (3)

Original: MultiArith [Roy and Roth, 2015], (4) AQUARAT [Ling et al., 2017], (5) GSM8K
Revised: MultiArith [Roy and Roth, 2015], (4) AQUARAT [Ling et al., 2017], (5) GSM8K

Original: [Cobbe et al., 2021], and (6) SVAMP [Patel et al., 2021].
Revised: [Cobbe et al., 2021], and (6) SVAMP [Patel et al., 2021].

Original: Thefirst three are from the classic Math World Problem Repository
Revised: Thefirst three are from the classic Math World Problem Repository

Original: [Koncel-Kedziorski et al., 2016],and the last three are from more recent benchmarks.
Revised: [Koncel-Kedziorski et al., 2016],and the last three are from more recent benchmarks.

Original: SingleEq and AddSub contain easier problems,which do not require multi-step calculation to solve the tasks.
Revised: SingleEq and AddSub contain easier problems,which do not require multi-step calculation to solve the tasks.

Original: MultiArith, AQUA-RAT, GSM8k, and
Revised: MultiArith, AQUA-RAT, GSM8k, and

Original: SVAMP are more challenging datasets that require multi-step reasoning to solve.
Revised: SVAMP are more challenging datasets that require multi-step reasoning to solve.

Original: For commonsense reasoning, we use CommonsenseQA
Revised: For commonsense reasoning, we use CommonsenseQA

Original: [Talmor et al., 2019] and StrategyQA
Revised: [Talmor et al., 2019] and StrategyQA

Original: [Gevaet al., 2021].
Revised: [Gevaet al., 2021].

Original: CommonsenseQA asks questions with complex semantics that often require reasoningbased on prior knowledge [Talmor et al., 2019].
Revised: CommonsenseQA asks questions with complex semantics that often require reasoningbased on prior knowledge [Talmor et al., 2019].

Original: StrategyQA requires models to infer an implicitmulti-hop reasoning to answer questions [Geva et al., 2021].
Revised: StrategyQA requires models to infer an implicitmulti-hop reasoning to answer questions [Geva et al., 2021].

Original: For symbolic reasoning, we use Last Letter Concatenation and Coin Flip [Wei et al., 2022].
Revised: For symbolic reasoning, we use Last Letter Concatenation and Coin Flip [Wei et al., 2022].

Original: Lastletter Concatenation asks the model to concatenate the last letters of each word.
Revised: Lastletter Concatenation asks the model to concatenate the last letters of each word.

Original: We used randomlyselected four names for each sample.
Revised: We used randomlyselected four names for each sample.

Original: Coin flip asks the model to answer whether a coin is still headsup after people either flip or do not flip the coin.
Revised: Coin Flip asks the model to answer whether a coin is still heads up after people either flip or do not flip the coin.

Original: We created samples of four times flip ordo not fliptrials.
Revised: We created samples of four times flip or not fliptrials.

Original: Although these tasks are easy for humans, language models typically exhibit a flat scalingcurve.
Revised: Although these tasks are easy for humans, LMs typically exhibit a flat scaling curve.

Original: For other logical reasoning tasks, we choose two evaluation sets from the BIG-bench effort [big,2021]:
Revised: For other logical reasoning tasks, we choose two evaluation sets from the BIG-bench effort [big,2021]:

Original: Date Understanding 1 and Tracking Shuffled Objects.
Revised: Date Understanding 1 and Tracking Shuffled Objects.

Original: Date Understanding asks models toinfer the date from a context.
Revised: Date Understanding asks models toinfer the date from a context.

Original: Tracking Shuffled Objects tests a model’s ability to infer the final stateof objects given its initial state and a sequence of object shuffling.
Revised: Tracking Shuffled Objects tests a model’s ability to infer the final stateof objects given its initial state and a sequence of object shuffling.

Original: We used a dataset of tracking threeshuffled objects for our experiment.
Revised: We used a dataset of tracking threeshuffled objects for our experiment.

Original: Models We experiment with 13 models in total: Instruct-GPT3 Ouyang et al.
Revised: Models We experiment with 17 models in total. Main experiments are conducted with InstructGPT3 Ouyang et al.

Original: {+[2022] (text-ada/babbage/curie/davinci-001 and text-davinci-002), original GPT
+}
Revised: {+[2022] (text-ada/babbage/curie/davinci-001 and text-davinci-002), original GPT
+}

Revised: Brown et al.

Original: [2022] and GPT3with four different model sizes (ada, babbage, curie, and davinci) 2 ,  GPT-2, GPT-Neo, GPT-J, T0[Sanh et al., 2022], and OPT [Zhang et al., 2022].
Revised: [2020] (ada, babbage, curie, and davinci) 2 , and PaLM Chowdhery et al. [2022] (8B,62B, and 540B). In addition, we used GPT-2, GPT-Neo, GPT-J, T0 [Sanh et al., 2022], and OPT[Zhang et al., 2022] for model scaling study.

Original: The size of LMs ranges from 0.3B to 175B. Weinclude both standard (e.g. GPT-3 and OPT), and instruction following variants (e.g. Instruct-GPT3and T0).
Revised: The size of LMs ranges from 0.3B to 540B. We includeboth standard (e.g. GPT-3 and OPT), and instruction following variants (e.g. Instruct-GPT3 and T0).

Original: See Appendix A.3 for details of model description.
Revised: See Appendix A.3 for details of model description.

Original: Without mentioning otherwise, we use Text-davinci-002 throughout the experiments.
Revised: Without mentioning otherwise, we usetext-davinci-002 throughout the experiments.

Original: Baselines We compare our Zero-shot-CoT mainly to standard Zero-shot prompting to verify theeffectiveness of its chain of thought reasoning.
Revised: Baselines We compare our Zero-shot-CoT mainly to standard Zero-shot prompting to verify theeffectiveness of its chain of thought reasoning.

Original: For Zero-shot experiments, similar answer promptsas Zero-shot-CoT are used as default.
Revised: For Zero-shot experiments, similar answer promptsas Zero-shot-CoT are used as default.

Original: See Appendix A.5 for detail.
Revised: See Appendix A.5 for detail.

Original: To better evaluate the zero-shotability of LLMs on reasoning tasks, we also compare our method to Few-shot and Few-shot-CoTbaselines from [Wei et al., 2022], using the same in-context examples.
Revised: To better evaluate the zero-shotability of LLMs on reasoning tasks, we also compare our method to Few-shot and Few-shot-CoTbaselines from [Wei et al., 2022], using the same in-context examples.

Original: Throughout the experiments,we use greedy decoding across all the methods.
Revised: Throughout the experiments,we use greedy decoding across all the methods.

Original: For the zero-shot approaches, the results are thereforedeterministic.
Revised: For the zero-shot approaches, the results are thereforedeterministic.

Original: For the few-shot approaches, since the order of in-context examples could affect theresults [Lu et al., 2022], we run each experiment only once with a fixed seed across all methods anddatasets, for fair comparisons with the zero-shot methods.
Revised: For the few-shot approaches, since the order of in-context examples could affect theresults [Lu et al., 2022], we run each experiment only once with a fixed seed across all methods anddatasets, for fair comparisons with the zero-shot methods.

Original: Wei et al.
Revised: Wei et al.

Original: [2022] showed that the order ofexamples did not cause large variance in CoT experiments.
Revised: [2022] showed that the order ofexamples did not cause large variance in CoT experiments.

Original: Answer cleansing After the model outputs a text by answer extraction (see § 3 and Figure 2), ourmethod picks up only the part of the answer text that first satisfies the answer format.
Revised: Answer cleansing After the model outputs a text by answer extraction (see § 3 and Figure 2), ourmethod picks up only the part of the answer text that first satisfies the answer format.

Original: For example,if the answer prompting outputs “probably 375 and 376” on arithmetic tasks, we extract the firstnumber “375” and set it as the model prediction.
Revised: For example,if the answer prompting outputs “probably 375 and 376” on arithmetic tasks, we extract the firstnumber “375” and set it as the model prediction.

Original: In the case of multiple-choice, the first large letterwe encounter is set as the prediction.
Revised: In the case of multiple-choice, the first large letterwe encounter is set as the prediction.

Original: See Appendix A.6 for more detail.
Revised: See Appendix A.6 for more detail.

Original: Standard Zero-shot methodfollows the same idea.
Revised: Standard Zero-shot methodfollows the same idea.

Original: For Few-shot and Few-shot-CoT methods, we follow [Wang et al., 2022] andfirst extract the answer text after "The answer is " from the model output, and apply the same answercleansing to parse the answer text.
Revised: For Few-shot and Few-shot-CoT methods, we follow [Wang et al., 2022] andfirst extract the answer text after "The answer is " from the model output, and apply the same answercleansing to parse the answer text.

Original: If “The answer is” is not found in the model output, we searchfrom the back of the text and set the first text that satisfies the answer format as the prediction.
Revised: If “The answer is” is not found in the model output, we searchfrom the back of the text and set the first text that satisfies the answer format as the prediction.

Original: 4.1 ResultsZero-shot-CoT vs. Zero-shot
Revised: 4.1 Results

Revised: Zero-shot-CoT vs. Zero-shot

Original: Table 1 summarize accuracy of our method (Zero-shot-CoT) andstandard zero-shot prompting (Zero-shot) for each dataset.
Revised: Table 1 summarize accuracy of our method (Zero-shot-CoT) andstandard zero-shot prompting (Zero-shot) for each dataset.

Original: Zero-shot-CoT substantially outperformsfour out of six arithmetic reasoning tasks (MultiArith, GSM8K, AQUA, SVAMP), all symbolicreasoning, and all other logical reasoning tasks (from BIG-bench [big, 2021]).
Revised: Zero-shot-CoT substantially outperformsfour out of six arithmetic reasoning tasks (MultiArith, GSM8K, AQUA, SVAMP), all symbolicreasoning, and all other logical reasoning tasks (from BIG-bench [big, 2021]).

Original: For example, Zeroshot-CoT achieves score gains from 17.7% to 78.7% on MultiArith and from 10.4% to 40.7% on
Revised: For example, Zeroshot-CoT achieves score gains from 17.7% to 78.7% on MultiArith and from 10.4% to 40.7% on

Original: GSM8K. Our method gives on-par performances for the remaining two arithmetic reasoning tasks(SingleEq and AddSub), which is expected since they do not require multi-step reasoning.
Revised: GSM8K. Our method gives on-par performances for the remaining two arithmetic reasoning tasks(SingleEq and AddSub), which is expected since they do not require multi-step reasoning.

Original: In commonsense reasoning tasks, Zero-shot-CoT does not provide performance gains.
Revised: In commonsense reasoning tasks, Zero-shot-CoT does not provide performance gains.

Original: It is expectedas Wei et al.
Revised: It is expectedas Wei et al.

Original: [2022] also reports that even Few-shot-CoT does not provide performance gains on
Revised: [2022] also reports that even Few-shot-CoT does not provide performance gains on

Original: Lambda (135B), but does improve StrategyQA when combined with substantially larger PaLM(540B) model, which may also apply for ours.
Revised: Lambda (135B), but does improve StrategyQA when combined with substantially larger PaLM(540B) model, which may also apply for ours.

Revised: More importantly, we observe that many generated

Revised: [Table]

Revised: experiment results with PaLM are found at Appendix D

Revised: MultiArith

Revised: GSM8K

Original: More importantly, we observe that many generatedchain of thought themselves are surprisingly logically correct or only contains human-understandablemistakes (see Table 4), suggesting that Zero-shot-CoT does elicit for better commonsense reasoning even when the task metrics do not directly reflect it.
Revised: [Table] chain of thought themselves are surprisingly logically correct or only contains human-understandablemistakes (see Table 3) [Table] suggesting that Zero-shot-CoT does elicit for better commonsense reasoningeven when the task metrics do not directly reflect it.

Original: We provide samples generated by Zero-shot-CoT for each dataset in Appendix B.
Revised: We provide samples generated by Zero-shot-CoTfor each dataset in Appendix B

Original: [Table]
Revised: [Table]

Original: [Table] Comparison with other baselines Table 2 compares the performances on two arithmetic reasoningbenchmarks (MultiArith and GSM8K) across Zero-shot-CoT and baselines.
Revised: Comparison with other baselines Table 2 compares the performances on two arithmetic reasoningbenchmarks (MultiArith and GSM8K) across Zero-shot-CoT and baselines.

Original: The large gap betweenstandard prompting (top block) and chain of thought prompting (middle block) suggests that thesetasks are difficult without eliciting multi-step reasoning.
Revised: The large gap betweenstandard prompting (1st block) and chain of thought prompting (2nd block) suggests that thesetasks are difficult without eliciting multi-step reasoning.

Revised: Major improvements are confirmed onboth Instruct GPT-3 (175B) and PaLM (540B) models (4th block).

Original: While Zero-shot-CoT naturally underperforms Few-shot-CoT, it substantially outperforms standard Few-shot prompting with even 8 examplesper task.
Revised: While Zero-shot-CoT naturallyunderperforms Few-shot-CoT, it substantially outperforms standard Few-shot prompting with even8 examples per task.

Original: For GSM8K, Zero-shot-CoT with Instruct GPT-3 (175B) also outperforms finetuned GPT-3and
Revised: For GSM8K, Zero-shot-CoT with Instruct GPT-3 (175B) also outperforms [Figure] [Table] [Table]

Original:  standard few-shot prompting with large models (PaLM, 540B), reported in Wei et al.
Revised: [Table] finetuned GPT-3 and standard few-shot prompting with large models (PaLM, 540B), reported in Weiet al.

Original: [2022](bottom block).
Revised: [2022] (3rd and 4th block).

Revised: Further experiment results with PaLM are found at Appendix D

Original: Error Analysis To better understand the behavior of Zero-shot-CoT, we manually investigatedrandomly selected examples generated by Instruct-GPT3 with Zero-shot-CoT prompting.
Revised: Error Analysis To better understand the behavior of Zero-shot-CoT, we manually investigatedrandomly selected examples generated by Instruct-GPT3 with Zero-shot-CoT prompting.

Original: See Appendix C for examples, where some of the observations include: (1) In commonsense reasoning(CommonsenseQA), Zero-shot-CoT often produces flexible and reasonable chain of thought evenwhen the final prediction is not correct.
Revised: See Appendix C for examples, where some of the observations include: (1) In commonsense reasoning(CommonsenseQA), Zero-shot-CoT often produces flexible and reasonable chain of thought evenwhen the final prediction is not correct.

Original: Zero-shot-CoT often output multiple answer choices whenthe model find it is difficult to narrow it down to one (see Table 4 for examples).
Revised: Zero-shot-CoT often output multiple answer choices whenthe model find it is difficult to narrow it down to one (see Table 3 for examples).

Original: In arithmeticreasoning (MultiArith), Zero-shot-CoT and Few-shot-CoT show substantial differences regarding [Table] [Table] the error patterns.
Revised: In arithmeticreasoning (MultiArith), Zero-shot-CoT and Few-shot-CoT show substantial differences regardingthe error patterns.

Original: First, it tends to output unnecessary steps of reasoning after getting the correctprediction, which results in changing the prediction to incorrect one.
Revised: First, it tends to output unnecessary steps of reasoning after getting the correctprediction, which results in changing the prediction to incorrect one.

Original: Zero-shot-CoT also sometimesdoes not start reasoning, just rephrasing the input question.
Revised: Zero-shot-CoT also sometimesdoes not start reasoning, just rephrasing the input question.

Original: In contrast, Few-shot-CoT tend to failwhen generated chain of thought include ternary operation, e.g. (3 + 2) ∗ 4 .
Revised: In contrast, Few-shot-CoT tend to failwhen generated chain of thought include ternary operation, e.g. (3 + 2) ∗ 4 .

Original: Does model size matter for zero-shot reasoning?
Revised: Does model size matter for zero-shot reasoning?

Original: Table 3 compares performance of variouslanguage models on MultiArith dataset.
Revised: Figure 3 compares performance of variouslanguage models on MultiArith / GSM8K 3 .

Original: Without chain of thought reasoning, the performance doesnot increase or increases slowly as the model scale is increased, i.e., the curve is mostly flat.
Revised: Without chain of thought reasoning, the performancedoes not increase or increases slowly as the model scale is increased, i.e., the curve is mostly flat.

Original: Incontrast, the performance drastically increases with chain of thought reasoning, as the model sizegets bigger.
Revised: Incontrast, the performance drastically increases with chain of thought reasoning, as the model sizegets bigger, for Original/Instruct GPT-3 and PaLM.

Original: When the model size is smaller, chain of thought reasoning is not effective.
Revised: When the model size is smaller, chain of thoughtreasoning is not effective.

Original: This resultaligns with the few-shot experiment results in Wei et al.
Revised: This result aligns with the few-shot experiment results in Wei et al.

Original: We also manually investigated thequality of generated chain of thought, and large-scale models clearly demonstrate better reasoning(see Appendix B for the sampled outputs for each model). 
Revised: We also manually investigated the quality of generated chain of thought, and large-scale modelsclearly demonstrate better reasoning (see Appendix B for the sampled outputs for each model). [Table] [Table] [Table]

Original: How does prompt selection affect Zero-shot-CoT?
Revised: How does prompt selection affect Zero-shot-CoT?

Original: We validate the robustness of Zero-shot-CoTagainst input prompts.
Revised: We validate the robustness of Zero-shotCoT against input prompts.

Original: Table 5 summarizes performance using eight different templates.
Revised: Table 4 summarizes performance using 16 different templates withthree categories.

Revised: Specifically, following Webson and Pavlick [2022], the categories are instructive(encourage reasoning), misleading (discourage reasoning or encouraging reasoning but in a wrongway), and irrelevant (nothing to do with reasoning).

Original: The resultsindicate that the performance is improved if the text is written in a way that encourages chain ofthought reasoning.
Revised: The results indicate that the performance isimproved if the text is written in a way that encourages chain of thought reasoning, i.e., the templatesare within "instructive" category.

Original: However, the difference in accuracy is significant depending on the sentence.
Revised: However, the difference in accuracy is significant depending onthe sentence.

Original: Inthis experiment, "Let’s think step by step."
Revised: In this experiment, "Let’s think step by step."

Original: achieves the best results.
Revised: achieves the best results.

Original: Interestingly, it is found thatdifferent templates encourage the model to express reasoning quite differently (see Appendix B forexample outputs by each template).
Revised: Interestingly,it is found that different templates encourage the model to express reasoning quite differently (see Appendix B for sample outputs by each template).

Revised: In contrast, when we use misleading or irrelevanttemplates, the performance does not improve.

Revised: It remains an open question how to automaticallycreate better templates for Zero-shot-CoT.

Original: How does prompt selection affect Few-shot-CoT?
Revised: How does prompt selection affect Few-shot-CoT?

Original: Table 6 shows the performance of Fewshot-CoT when using examples from different datasets: CommonsenseQA to AQUA-RAT and
Revised: Table 5 shows the performance of Fewshot-CoT when using examples from different datasets: CommonsenseQA to AQUA-RAT and

Original: CommonsenseQA to MultiArith.
Revised: CommonsenseQA to MultiArith.

Original: The domains are different in both cases, but the answer formatis the same in the former.
Revised: The domains are different in both cases, but the answer formatis the same in the former.

Original: Surprisingly, the chain of thought examples from different domains

Original: [Table] [Table] [Table] (common sense to arithmetic) but with the same answer (multiple-choice) format provide substantialperformance gain over Zero-shot (to AQUA-RAT), measured relative to the possible improvementsfrom Zero-shot-CoT or Few-shot-CoT.
Revised: Surprisingly, the chain of thought examples from different domains(common sense to arithmetic) but with the same answer (multiple-choice) format provide substantialperformance gain over Zero-shot (to AQUA-RAT), measured relative to the possible improvementsfrom Zero-shot-CoT or Few-shot-CoT.

Original: In contrast, the performance gain becomes much less whenusing examples with different answer types (to MultiArith), confirming prior work [Min et al., 2022]that suggests LLMs mostly leverage the few-shot examples to infer the repeated format rather thanthe task itself in-context.
Revised: In contrast, the performance gain becomes much less whenusing examples with different answer types (to MultiArith), confirming prior work [Min et al., 2022]that suggests LLMs mostly leverage the few-shot examples to infer the repeated format rather thanthe task itself in-context.

Original: Nevertheless, for both cases the results are worse than Zero-shot-CoT,affirming the importance of task-specific sample engineering in Few-shot-CoT.
Revised: Nevertheless, for both cases the results are worse than Zero-shot-CoT,affirming the importance of task-specific sample engineering in Few-shot-CoT.

Original:  Discussion and Related Work
Revised:  Discussion and Related Work

Original: Reasoning Ability of LLMs Several studies have shown that pre-trained models usually are notgood at reasoning [Brown et al., 2020, Smith et al., 2022, Rae et al., 2021], but its ability can besubstantially increased by making them produce step-by-step reasoning, either by fine-tuning [Rajaniet al., 2019, Cobbe et al., 2021, Zelikman et al., 2022, Nye et al., 2022] or few-shot prompting [Weiet al., 2022, Wang et al., 2022, Chowdhery et al., 2022]
Revised: Reasoning Ability of LLMs Several studies have shown that pre-trained models usually are notgood at reasoning [Brown et al., 2020, Smith et al., 2022, Rae et al., 2021], but its ability can besubstantially increased by making them produce step-by-step reasoning, either by fine-tuning [Rajaniet al., 2019, Cobbe et al., 2021, Zelikman et al., 2022, Nye et al., 2022] or few-shot prompting [Weiet al., 2022, Wang et al., 2022, Chowdhery et al., 2022]

Original: (Table 7).
Revised: (See Table 27 for summary of related work).

Original: Unlike most prior work, wefocus on zero-shot prompting and show that a single fixed trigger prompt substantially increasesthe zero-shot reasoning ability of LLMs across a variety of tasks requiring complex multi-hopthinking (Table 1), especially when the model is scaled up (Table 3).
Revised: Unlike most prior work, we focus on zero-shot prompting and show that a single fixed trigger promptsubstantially increases the zero-shot reasoning ability of LLMs across a variety of tasks requiringcomplex multi-hop thinking (Table 1), especially when the model is scaled up (Figure 3).

Original: It also generates reasonable andunderstandable chain of thought across diverse tasks (Appendix B), even when the final predictionis wrong (Appendix C).
Revised: It alsogenerates reasonable and understandable chain of thought across diverse tasks (Appendix B), evenwhen the final prediction is wrong (Appendix C).

Original: Similar to our work, Reynolds and McDonell [2021] demonstrate a prompt,“Let’s solve this problem by splitting it into steps”, would facilitate the multi-step reasoning ina simple arithmetic problem.
Revised: Similar to our work, Reynolds and McDonell[2021] demonstrate a prompt, “Let’s solve this problem by splitting it into steps”, would facilitatethe multi-step reasoning in a simple arithmetic problem.

Original: However, they treated it as a task-specific example and did notevaluate quantitatively on diverse reasoning tasks against baselines.
Revised: However, they treated it as a task-specificexample and did not evaluate quantitatively on diverse reasoning tasks against baselines.

Original: Shwartz et al. [2020] proposeto decompose a commonsense question into a series of information seeking question, such as“what is the definition of [X] ”.
Revised: Shwartz et al.[2020] propose to decompose a commonsense question into a series of information seeking question,such as “what is the definition of [X] ”.

Original: It does not require demonstrations but requires substantial manualprompt engineering per each reasoning task.
Revised: It does not require demonstrations but requires substantialmanual prompt engineering per each reasoning task.

Original: Our results strongly suggest that LLMs are decentzero-shot reasoners, while prior work [Wei et al., 2022] often emphasize only few-shot learningand task-specific in-context learning, e.g. no zero-shot baselines were reported.
Revised: Our results strongly suggest that LLMs aredecent zero-shot reasoners, while prior work [Wei et al., 2022] often emphasize only few-shot learningand task-specific in-context learning, e.g. no zero-shot baselines were reported.

Original: Our method doesnot require time-consuming fine tuning or [Table]
Revised: Our method doesnot require time-consuming fine-tuning or expensive sample engineering, and can be combined withany pre-trained LLM, serving as the strongest zero-shot baseline for all reasoning tasks.

Original: Zero-shot Abilities of LLMs
Revised: Zero-shot Abilities of LLMs

Original: Radford et al.
Revised: Radford et al.

Original: [2019] show that LLMs have excellent zero-shotabilities in many system-1 tasks, including reading comprehension, translation [Table] and summarization.
Revised: [2019] show that LLMs have excellent zero-shotabilities in many system-1 tasks, including reading comprehension, translation, and summarization.

Original: Sanh et al.
Revised: Sanh et al.

Original: [2022], Ouyang et al.
Revised: [2022], Ouyang et al.

Original: [2022] show that such zero-shot abilities of LLMs can be increasedby explicitly fine-tuning models to follow instructions.
Revised: [2022] show that such zero-shot abilities of LLMs can be increasedby explicitly fine-tuning models to follow instructions.

Original: Although these work focus on the zero-shotperformances of LLMs, we focus on many system-2 tasks beyond system-1 tasks, considered a grandchallenge for LLMs given flat scaling curves.
Revised: Although these work focus on the zero-shotperformances of LLMs, we focus on many system-2 tasks beyond system-1 tasks, considered a grandchallenge for LLMs given flat scaling curves.

Original: In addition, Zero-shot-CoT is orthogonal to instructiontuning; it increases zero-shot performance forboth Instruct GPT3 and vanilla GPT3 (Table 3).
Revised: In addition, Zero-shot-CoT is orthogonal to instructiontuning; it increases zero-shot performance for Instruct GPT3, vanilla GPT3, and PaLM (See Figure 3).

Original: From Narrow (task-specific) to Broad (multi-task)
Revised: From Narrow (task-specific) to Broad (multi-task)

Original: Prompting Most prompts are task-specific.
Revised: Prompting Most prompts are task-specific.

Original: While few-shot prompts are naturally so due to task-specific in-context samples [Brown et al., 2020,
Revised: While few-shot prompts are naturally so due to task-specific in-context samples [Brown et al., 2020,

Original: Wei et al., 2022], majority of zero-shot prompts have also focused on per-task engineering (oftemplates)
Revised: Wei et al., 2022], majority of zero-shot prompts have also focused on per-task engineering (oftemplates)

Original: [Liu et al., 2021b, Reynolds and McDonell, 2021].
Revised: [Liu et al., 2021b, Reynolds and McDonell, 2021].

Original: Borrowing terminologies from Chollet[2019] which builds on hierarchical models of intelligence [McGrew, 2005, Johnson and Bouchard Jr,2005], these prompts are arguably eliciting “narrow generalization” or task-specific skills from LLMs.
Revised: Borrowing terminologies from Chollet[2019] which builds on hierarchical models of intelligence [McGrew, 2005, Johnson and Bouchard Jr,2005], these prompts are arguably eliciting “narrow generalization” or task-specific skills from LLMs.

Original: On the other hand, our method is a multi-task prompt and elicits “broad generalization” or broadcognitive abilities in LLMs, such as logical reasoning or system-2 itself.
Revised: On the other hand, our method is a multi-task prompt and elicits “broad generalization” or broadcognitive abilities in LLMs, such as logical reasoning or system-2 itself.

Original: We hope our work can serveas a reference for accelerating not just logical reasoning research with LLMs, but also discovery ofother broad cognitive capabilities within LLMs.
Revised: We hope our work can serveas a reference for accelerating not just logical reasoning research with LLMs, but also discovery ofother broad cognitive capabilities within LLMs.

Original: Limitation and Social Impact
Revised: Limitation and Social Impact

Original: Our work is based on prompting methods for large language models.
Revised: Our work is based on prompting methods for large language models.

Original: LLMs have been trained on large corpora from various sources on the web, and have shown to captureand amplify biases found in the training data.
Revised: LLMs have been trained on large corpora from various sources on the web (Also see subsection F.2),and have shown to capture and amplify biases found in the training data.

Original: Prompting is a method that looks to take advantageof the patterns captured by language models conducive to various tasks, and therefore it has thesame shortcomings.
Revised: Prompting is a method thatlooks to take advantage of the patterns captured by language models conducive to various tasks, andtherefore it has the same shortcomings.

Original: This being said, our approach is a more direct way to probe complex reasoninginside pre-trained LLMs, removing the confounding factor of in-context learning in prior few-shotapproaches, and can lead to more unbiased study of biases in LLMs.
Revised: This being said, our approach is a more direct way to probecomplex reasoning inside pre-trained LLMs, removing the confounding factor of in-context learningin prior few-shot approaches, and can lead to more unbiased study of biases in LLMs.

Original:  Conclusion
Revised:  Conclusion

Original: We have proposed Zero-shot-CoT, a single zero-shot prompt that elicits chain of thought from largelanguage models across a variety of reasoning tasks, in contrast to the few-shot (in-context) approachin previous work that requires hand-crafting few-shot examples per task.
Revised: We have proposed Zero-shot-CoT, a single zero-shot prompt that elicits chain of thought from largelanguage models across a variety of reasoning tasks, in contrast to the few-shot (in-context) approachin previous work that requires hand-crafting few-shot examples per task.

Original: Our simple method not onlyis the minimalist and strongest zero-shot baseline for difficult multi-step system-2 reasoning tasksthat long evaded the scaling laws of LLMs, but also encourages the community to further discoversimilar multi-task prompts that elicit broad cognitive abilitieslike logical reasoning instead of narrowtask-specific skills.
Revised: Our simple method not onlyis the minimalist and strongest zero-shot baseline for difficult multi-step system-2 reasoning tasksthat long evaded the scaling laws of LLMs, but also encourages the community to further discoversimilar multi-task prompts that elicit broad cognitive abilities instead of narrow task-specific skills.

Original: Checklist
Revised: Checklist

Original: For all authors...
Revised: For all authors...

Original: (a) Do the main claims made in the abstract and introduction accurately reflect the paper’s
Revised: (a) Do the main claims made in the abstract and introduction accurately reflect the paper’s

Original: contributions and scope?
Revised: contributions and scope?

Original: [Yes]
Revised: [Yes]

Original: (b) Did you describe the limitations of your work?
Revised: (b) Did you describe the limitations of your work?

Original: [Yes]
Revised: [Yes]

Original: (c) Did you discuss any potential negative societal impacts of your work?
Revised: (c) Did you discuss any potential negative societal impacts of your work?

Original: [Yes]
Revised: [Yes]

Original: (d) Have you read the ethics review guidelines and ensured that your paper conforms tothem?
Revised: (d) Have you read the ethics review guidelines and ensured that your paper conforms to them?

Original: [Yes]
Revised: [Yes]

Original: If you are including theoretical results...
Revised: If you are including theoretical results...

Original: (a) Did you state the full set of assumptions of all theoretical results?
Revised: (a) Did you state the full set of assumptions of all theoretical results?

Original: [N/A]
Revised: [N/A]

Original: (b) Did you include complete proofs of all theoretical results?
Revised: (b) Did you include complete proofs of all theoretical results?

Original: [N/A]
Revised: [N/A]

Original: If you ran experiments...
Revised: If you ran experiments...

Original: (a) Did you include the code, data, and instructions needed to reproduce the main experi-
Revised: (a) Did you include the code, data, and instructions needed to reproduce the main experi-

Original: mental results (either in the supplemental material or as a URL)?
Revised: mental results (either in the supplemental material or as a URL)?

Original: [Yes]
Revised: [Yes]

Original: (b) Did you specify all the training details (e.g., data splits, hyperparameters,
Revised: (b) Did you specify all the training details (e.g., data splits, hyperparameters,

Original: how they
Revised: how they

Original: were chosen)?
Revised: were chosen)?

Original: [Yes]
Revised: [Yes]

Original: (c) Did you report error bars (e.g., with respect to the random seed after running experi- ments multiple times)?
Revised: (c) Did you report error bars (e.g., with respect to the random seed after running experiments multiple times)?

Original: [No] Our paper mainly used GPT-3 API with greedy decoding,
Revised: [No] Our paper mainly used GPT-3 API with greedy decoding,

Original: and there are no randomness for the experiments.
Revised: and there are no randomness for the experiments.

Original: (d) Did you include the total amount of compute and the type of resources used (e.g., type
Revised: (d) Did you include the total amount of compute and the type of resources used (e.g., type

Original: of GPUs, internal cluster, or cloud provider)?
Revised: of GPUs, internal cluster, or cloud provider)?

Original: [Yes]
Revised: [Yes]

Original: If you are using existing assets (e.g., code, data, models) or curating/releasing new assets...
Revised: If you are using existing assets (e.g., code, data, models) or curating/releasing new assets...

Original: (a) If your work uses existing assets, did you cite the creators?
Revised: (a) If your work uses existing assets, did you cite the creators?

Original: [Yes]
Revised: [Yes]

Original: (b) Did you mention the license of the assets?
Revised: (b) Did you mention the license of the assets?

Original: [Yes]
Revised: [Yes]

Original: (c) Did you include any new assets either in the supplemental material or as a URL?
Revised: (c) Did you include any new assets either in the supplemental material or as a URL?

Original: [Yes]
Revised: [Yes]

Original: (d) Did you discuss whether and how consent was obtained from people whose data you’re
Revised: (d) Did you discuss whether and how consent was obtained from people whose data you’re

Original: using/curating?
Revised: using/curating?

Original: [Yes]
Revised: [Yes]

Original: (e) Did you discuss whether the data you are using/curating contains personally identifiable
Revised: (e) Did you discuss whether the data you are using/curating contains personally identifiable

Original: information or offensive content?
Revised: information or offensive content?

Original: [Yes]
Revised: [Yes]

Original: If you used crowdsourcing or conducted research with human subjects...
Revised: If you used crowdsourcing or conducted research with human subjects...

Original: (a) Did you include the full text of instructions given to participants and screenshots, ifapplicable?
Revised: (a) Did you include the full text of instructions given to participants and screenshots, if applicable?

Original: [N/A]
Revised: [N/A]

Original: (b) Did you describe any potential participant risks, with links to Institutional Review
Revised: (b) Did you describe any potential participant risks, with links to Institutional Review

Original: IRB , if applicable?
Revised: Board (IRB) approvals, if applicable?

Original: [N/A
Revised: [N/A]

Original: (c) Did you include the estimated hourly wage paid to participants and the total amount
Revised: (c) Did you include the estimated hourly wage paid to participants and the total amount

Original: spent on participant compensation?
Revised: spent on participant compensation?

Original: [N/A]
Revised: [N/A]