Large Language Models are Zero-Shot Reasoners

24 May 2022  ·  Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, Yusuke Iwasawa ·

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.

PDF Abstract

Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Arithmetic Reasoning GSM8K Text-davinci-002-175B (zero-plus-few-Shot-cot (8 samples)) Accuracy 51.5 # 40
Parameters (Billion) 175 # 23
Arithmetic Reasoning GSM8K PaLM-540B (few-Shot-cot) Accuracy 58.1 # 32
Parameters (Billion) 540 # 31
Arithmetic Reasoning GSM8K Finetuned GPT-3 175B + verifier Accuracy 55.0 # 37
Parameters (Billion) 175 # 23
Arithmetic Reasoning GSM8K Text-davinci-002-175B (zero-shot) Accuracy 10.4 # 56
Parameters (Billion) 175 # 23
Arithmetic Reasoning GSM8K PaLM 540B (few-shot) Accuracy 17.9 # 51
Parameters (Billion) 540 # 31
Arithmetic Reasoning GSM8K Text-davinci-002-175B (zero-shot-cot) Accuracy 40.7 # 43
Parameters (Billion) 175 # 23
Arithmetic Reasoning GSM8K Text-davinci-002-175B (few-shot-cot (2 samples)) Accuracy 41.3 # 42
Parameters (Billion) 175 # 23
Arithmetic Reasoning MultiArith Text-davinci-002 (175B) (zero-shot) Accuracy 17.7 # 2
Arithmetic Reasoning MultiArith Text-davinci-002 (175B)(zero-shot-cot) Accuracy 78.7 # 1
Math Word Problem Solving SVAMP PaLM (zero-shot, CoT) Execution Accuracy 62.1 # 5
Math Word Problem Solving SVAMP PaLM (zero-shot) Execution Accuracy 58.8 # 6

Methods