Binder🔗: Binding Language Models in Symbolic Languages
Binder is an innovative project designed to bind language models with symbolic languages. This project uses program annotations to achieve state-of-the-art or comparable performance, offering users an opportunity to explore and understand complex language models in a more connected and intuitive manner.
Project Background
The foundation of Binder is a research paper titled Binding Language Models in Symbolic Languages. It highlights a novel approach to enable language models to work seamlessly with symbolic expressions. The project builds on the concept of simplifying and making the interaction with complex language models more feasible for users through a symbolic interface.
Key Updates
- August 25, 2023: Binder was updated to support models from OpenAI's chat series, such as
gpt-3.5-xxxandgpt-4-xxx. This expansion in support underscores Binder's adaptability and future-ready structure. - March 23, 2023: Due to the discontinuation of Codex series models by OpenAI, the focus shifted toward testing and updating the engine from "code-davinci-002" to "gpt-3.5-turbo".
- January 22, 2023: The project received recognition by being accepted at the ICLR 2023 conference, spotlighting its importance and value in the academic field.
- October 6, 2022: The project's code, demo, and dedicated website were publicly released, opening the doors for wider community engagement and participation.
Setting Up Binder
To utilize Binder, users are required to set up an environment. This begins with creating an environment named binder through a series of shell commands. Installing the necessary packages is crucial for ensuring smooth functionality.
conda env create -f py3.7binder.yaml
pip install records==0.5.3
After setting up the environment, users need to activate it using the command:
conda activate binder
API Key Integration
An essential step in using Binder is acquiring API keys from OpenAI API. This key should be stored in a key.txt file. Users must ensure they have the necessary permissions to access the models available in Binder, specifically the code-davinci-002 model at present.
Running Binder
To run Binder, users should explore and utilize commands available in the run.py script. This script is a crucial component of the Binder environment, providing the operational flexibility needed to initiate different processes within the project.
Closing Remarks
Binder is an effective tool for bridging the gap between advanced language models and symbolic languages. By incorporating contemporary updates and facilitating a simplified user experience, the project represents a significant stride in the computational linguistics domain. As new developments continue to emerge, Binder remains at the forefront, ready to adapt and grow in its capabilities.
Acknowledgments
If Binder's contributions have been beneficial, users are encouraged to cite the project using the following format:
@article{Binder,
title={Binding Language Models in Symbolic Languages},
author={Zhoujun Cheng et al.},
journal={ICLR},
year={2023}
}
Finally, the engaging team behind Binder deserves recognition for their dedication and hard work. They have made it possible to bring complex language models and symbolic expressions into a more cohesive and accessible framework.