Large Language Models are Zero-Shot Reasoners
This is the official implementation of Large Language Models are Zero-Shot Reasoners .
Installation
Make sure you have Python>=3.8 installed on your machine.
pip install torch==1.8.2+cu111 torchtext==0.9.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
pip install -r requirements.txt
Set your OpenAI API key
# https://beta.openai.com/account/api-keys
export OPENAI_API_KEY=(YOUR OPENAI API KEY)
Set arguments.
model=gpt3-xl # {"gpt3", "gpt3-medium", "gpt3-large", "gpt3-xl"}. "gpt3" is the smallest model.
dataset=multiarith # We can use other datasets. See help for the details.
limit_dataset_size=10 # This is important to save your budget. If you want to use all the samples in a dataset, set 0.
api_time_interval=1.0 # Caution. The API allows users request API up to 60 times in a minutes, otherwise errors happen.
Quick Start
Zero-shot-CoT (our proposal)
python main.py --method=zero_shot_cot --model=${model} --dataset=${dataset}
Zero-shot
python main.py --method=zero_shot --model=${model} --dataset=${dataset}
Few-shot-CoT
# MultiArith and GSM8K are currently available.
python main.py --method=few_shot_cot --model=${model} --dataset=${dataset}
Few-shot
# MultiArith and GSM8K are currently available.
python main.py --method=few_shot --model=${model} --dataset=${dataset}