GLTR
GLTR Overview
GLTR, developed by a collaboration of MIT-IBM Watson AI lab and HarvardNLP, is a tool designed to detect and visualize automatically generated text. It uses the same models that generate fake text to detect it, specifically the GPT-2 117M language model from OpenAI. GLTR analyzes textual input and predicts what GPT-2 would have predicted at each position, using this information to overlay a colored mask over the text that corresponds to the position in the ranking. This allows for a direct visual indication of how likely each word was under the model. GLTR is a powerful tool in the fight against the misuse of AI in generating fake reviews, comments, or news articles.
GLTR Highlights
- GLTR uses the same models that generate fake text to detect it, providing a unique approach to identifying automatically generated text.
- The tool provides a visual representation of the likelihood of each word in a text being generated by an AI, making it user-friendly and easily interpretable.
- GLTR can be used to analyze any textual input, providing a versatile tool for detecting AI-generated text in a variety of contexts.
Use Cases
An e-commerce business uses GLTR to analyze customer reviews on their products. The tool is used to scan through the reviews and identify any that are likely to have been automatically generated by AI.
The business is able to identify and remove fake reviews, ensuring that their product ratings are accurate and reliable. This helps to maintain trust with their customers and protect their brand reputation.
A social media platform uses GLTR to analyze comments and posts made on their platform. The tool is used to detect any text that is likely to have been automatically generated by AI, which could indicate spam or malicious activity.
The platform is able to identify and remove AI-generated spam, improving the user experience and maintaining the integrity of their platform.
A news organization uses GLTR to analyze articles submitted for publication. The tool is used to detect any text that is likely to have been automatically generated by AI, which could indicate false or misleading information.
The organization is able to identify and reject AI-generated articles, ensuring that their news content is accurate and trustworthy.