We introduce Binoculars, a state-of-the-art method for detecting AI-generated text. Binoculars is a zero-shot and domain-agnostic (requires no training data) method. It is based on a simple idea: most decoder-only, causal language models have a huge overlap in pretraining datasets, for e.g. Common Crawl, Pile, etc. More details about the method and results can be found in our paper Spotting LLMs with Binoculars: Zero-Shot Detection of Machine-Generated Text.
To run the implementation of Binoculars, you can clone this repository and install the package using pip. This code was developed and tested on Python This code was developed and tested with Python 3.9. To install the package, run the following commands:
$ git clone https://github.com/ahans30/Binoculars.git
$ cd Binoculars
$ pip install -e .Please note, this implementation comes with a fixed global threshold that is used to classify the input as AI-generated
or not. This threshold is selected using Falcon-7B and Falcon-7B-Instruct models for scoring. If you want to
use different scoring models, you can pass it as an argument to the Binoculars class. Please read the paper for more
details about the Binoculars work.
To detect AI-generated text, please use the following code snippet:
from binoculars import Binoculars
bino = Binoculars()
# ChatGPT (GPT-4) output when prompted with “Can you write a few sentences about a capybara that is an astrophysicist?"
sample_string = '''Dr. Capy Cosmos, a capybara unlike any other, astounded the scientific community with his
groundbreaking research in astrophysics. With his keen sense of observation and unparalleled ability to interpret
cosmic data, he uncovered new insights into the mysteries of black holes and the origins of the universe. As he
peered through telescopes with his large, round eyes, fellow researchers often remarked that it seemed as if the
stars themselves whispered their secrets directly to him. Dr. Cosmos not only became a beacon of inspiration to
aspiring scientists but also proved that intellect and innovation can be found in the most unexpected of creatures.'''
print(bino.compute_score(sample_string)) # 0.75661373
print(bino.predict(sample_string)) # 'Most likely AI-Generated'In the above code, user can also pass a list of str to compute_score and predict methods to get results for
the entire batch of samples.
We have also made a demo available to predict AI-generated text interactively with a simple UI using gradio. You can run the demo using the following command:
$ python app.pyAll AI-generated text detectors aim for accuracy, but none are perfect and can have multiple failure modes (e.g., Binoculars is more proficient in detecting English language text compared to other languages). This implementation is for academic purposes only and should not be considered as a consumer product. We also strongly caution against using Binoculars (or any detector) without human supervision.
If you find this work useful, please cite our paper:
@misc{hans2024spotting,
title={Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text},
author={Abhimanyu Hans and Avi Schwarzschild and Valeriia Cherepanova and Hamid Kazemi and Aniruddha Saha and Micah Goldblum and Jonas Geiping and Tom Goldstein},
year={2024},
eprint={2401.12070},
archivePrefix={arXiv},
primaryClass={cs.CL}
}