https://github.com/kojima-takeshi188/zero_shot_cot

Prod Env
https://github.com/kojima-takeshi188/zero_shot_cot

Last synced: about 2 months ago
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Prod Env

• Host: GitHub
• URL: https://github.com/kojima-takeshi188/zero_shot_cot
• Owner: kojima-takeshi188
• Created: 2022-05-23T23:27:58.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2023-10-09T20:03:44.000Z (about 1 year ago)
• Last Synced: 2024-08-01T02:25:40.021Z (4 months ago)
• Language: Python
• Size: 3.75 MB
• Stars: 364
• Watchers: 2
• Forks: 56
• Open Issues: 6
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• Awesome-Reasoning-Foundation-Models - [Code

README

        
# Large Language Models are Zero-Shot Reasoners

This is the official implementation of `Large Language Models are Zero-Shot Reasoners` (Accepted at NeurIPS 2022).

The paper is available at [NeurIPS 2022](https://proceedings.neurips.cc/paper_files/paper/2022/hash/8bb0d291acd4acf06ef112099c16f326-Abstract-Conference.html) and [arXiv](https://arxiv.org/abs/2205.11916).







## 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.

```

# Notes: this experiment uses InstructGPT models.

# https://platform.openai.com/docs/model-index-for-researchers

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}

```

## Citation

```

@inproceedings{NEURIPS2022_8bb0d291,

 author = {Kojima, Takeshi and Gu, Shixiang (Shane) and Reid, Machel and Matsuo, Yutaka and Iwasawa, Yusuke},

 booktitle = {Advances in Neural Information Processing Systems},

 pages = {22199--22213},

 title = {Large Language Models are Zero-Shot Reasoners},

 volume = {35},

 year = {2022}

}

```