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Auto-CoT: Automatic Chain of Thought Prompting in Large Language Models (ICLR 2023)

Open Auto-CoT in Colab

Cheer AI up with the "let's think step by step" prompt? More plz. Let’s think not just step by step, but also one by one.

Auto-CoT uses more cheers & diversity to SAVE huge manual efforts in chain of thought prompt design, matching or even exceeding performance of manual design on GPT-3.

Check out our 25-page paper for more information.

Requirements

Python>=3.8

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

Datasets

Download the datasets from the following:

https://github.com/kojima-takeshi188/zero_shot_cot/tree/main/dataset
https://github.com/kojima-takeshi188/zero_shot_cot/tree/main/log

Quick Start

See try_cot.ipynb

Instructions

Construct Demos:

python run_demo.py \
--task multiarith \
--pred_file log/multiarith_zero_shot_cot.log \
--demo_save_dir demos/multiarith

Run inference:

python run_inference.py \
--dataset multiarith \
--demo_path demos/multiarith \
--output_dir experiment/multiarith

Citing Auto-CoT

@inproceedings{zhang2023automatic,
  title={Automatic Chain of Thought Prompting in Large Language Models},
  author={Zhang, Zhuosheng and Zhang, Aston and Li, Mu and Smola, Alex},
  booktitle={The Eleventh International Conference on Learning Representations (ICLR 2023)},
  year={2023}
}

Security

See CONTRIBUTING for more information.

License

This project is licensed under the Apache-2.0 License.