[ICLR 2023] Code for the paper "Binding Language Models in Symbolic Languages"
Code for paper Binding Language Models in Symbolic Languages. Please refer to our project page for more demonstrations and up-to-date related resources. Check out our demo page to have an instant experience of Binder, which achieves sota or comparable performance with only dozens of(~10) program annotations.
gpt-3.5-xxx
and gpt-4-xxx
, code will be further refactor later to support more!To establish the environment run this code in the shell:
conda env create -f py3.7binder.yaml
pip install records==0.5.3
That will create the environment binder
we used.
Activate the environment by running
conda activate binder
Apply and get API keys
(sk-xxxx like) from OpenAI API, save the key in key.txt
file, make sure you have the rights to access the model(in the implementation of this repo, code-davinci-002
) you need.
Check out commands in run.py
If you find our work helpful, please cite as
@article{Binder,
title={Binding Language Models in Symbolic Languages},
author={Zhoujun Cheng and Tianbao Xie and Peng Shi and Chengzu Li and Rahul Nadkarni and Yushi Hu and Caiming Xiong and Dragomir Radev and Mari Ostendorf and Luke Zettlemoyer and Noah A. Smith and Tao Yu},
journal={ICLR},
year={2023},
volume={abs/2210.02875}
}