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.
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
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
See try_cot.ipynb
Construct Demos:
python run_demo.py \
--task multiarith \
--pred_file log/multiarith_zero_shot_cot.log \
--demo_save_dir demos/multiarith
python run_demo.py \
--task multiarith \
--pred_file log/multiarith_zero_shot_cot.log \
--clustering_method hierarchical \
--demo_save_dir demos/multiarith_clustering_method=hierarchical
python run_demo.py \
--task multiarith \
--pred_file log/multiarith_zero_shot_cot.log \
--clustering_method seat \
--demo_save_dir demos/multiarith_clustering_method=seat
Run inference:
python run_inference.py \
--dataset multiarith \
--demo_path demos/multiarith \
--output_dir experiment/multiarith
python run_inference.py \
--dataset multiarith \
--demo_path demos/multiarith_clustering_method=hierarchical \
--output_dir experiment/multiarith_clustering_method=hierarchical
python run_inference.py \
--dataset multiarith \
--demo_path demos/multiarith_clustering_method=seat \
--output_dir experiment/multiarith_clustering_method=seat
@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}
}
See CONTRIBUTING for more information.
This project is licensed under the Apache-2.0 License.