Large Language Models are Zero-Shot Reasoners

Large Language Models are Zero-Shot Reasoners
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Original Paper
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.

Summary Notes

Unveiling the Power of Language Models: A Leap in AI's Zero-Shot Reasoning

The field of Natural Language Processing (NLP) is witnessing rapid advancements, thanks to the growth of Large Language Models (LLMs) like GPT-3. These models have transformed how AI understands and generates text similar to humans.
A particularly exciting development is their ability to tackle complex reasoning tasks directly, without needing specific training for each task. This capability is largely attributed to a method known as zero-shot learning.
This blog post explores Zero-shot Chain of Thought (Zero-shot-CoT), an innovative approach enhancing LLMs' reasoning abilities, drawing significant interest from AI Engineers in large companies.

Evolution of Prompting in LLMs

LLMs are known for producing responses that are both coherent and contextually relevant for a wide range of tasks. The technique of "prompting" these models with a question or context, followed by a cue for response, has been crucial.
Among these techniques, Chain of Thought (CoT) prompting has shown promise in enabling multi-step reasoning by guiding models through logical steps.
However, CoT's reliance on multiple, task-specific examples has limited its widespread use.

Zero-shot Chain of Thought (Zero-shot-CoT) Explained

Zero-shot-CoT stands out by streamlining the prompting process. It uses simple cues like "Let's think step by step" to initiate a reasoning path within the model, avoiding the need for task-specific examples. This makes it a more flexible and scalable approach compared to traditional CoT.

Key Features of Zero-shot-CoT:

  • Task-Agnostic: Works across different reasoning tasks without custom tuning.
  • Simple Prompt Engineering: Enables complex reasoning with straightforward prompts.
  • Enhanced Reasoning Ability: Supports sophisticated multi-step reasoning processes.

Methodology: A Two-Stage Prompting Strategy

Zero-shot-CoT utilizes a two-stage prompting technique. Initially, it prompts the LLM to outline a reasoning path to approach a question. Then, a second prompt helps derive the final answer from this reasoning path.
This method has been tested on various reasoning tasks, including arithmetic and commonsense reasoning, showing its versatility.

Experimentation and Results

The effectiveness of Zero-shot-CoT is backed by solid evidence. It outperforms traditional zero-shot and few-shot learning methods across different datasets and matches the efficiency of few-shot CoT without needing task-specific examples.

Experimental Highlights:

  • Broad Applicability: Shows significant performance improvement across numerous reasoning tasks.
  • Superior to Existing Methods: Matches or exceeds the performance of few-shot learning methods, setting a new standard for zero-shot reasoning.

Implications and Future Directions

Zero-shot-CoT offers significant benefits for AI Engineers, especially in enterprise applications. It simplifies using LLMs for complex reasoning tasks and broadens the potential applications of these models.

Opportunities Unlocked:

  • Less Need for Task-Specific Tuning: Reduces dependence on large, specific datasets for training.
  • Wider Application of LLMs: Makes pre-trained models useful for more tasks, increasing their enterprise value.

Conclusion: The Impact of Zero-shot-CoT

Zero-shot-CoT marks a significant milestone in NLP, offering a powerful method for extracting advanced reasoning from LLMs.
It lowers the barriers to using sophisticated AI technologies in enterprises by making complex reasoning more accessible.


The development of Zero-shot-CoT was supported by leading institutions and advanced computational resources.
For those interested in the technical details, the original study's appendix provides an in-depth look at the experimental setups and findings, showcasing the thorough research behind this innovation.
As we enter a new era in NLP, Zero-shot-CoT highlights the potential of simple prompt engineering to unlock the reasoning capabilities of LLMs, setting a new benchmark for AI solutions in the business world.

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