Forensically detects auto-generated text.
Generated by ChatGPT

GLTR is a tool developed by the MIT-IBM Watson AI lab and HarvardNLP that can detect automatically generated text using forensic analysis. It detects when a text has been artificially generated by analyzing how likely it is that a language model has generated the text.

GLTR visually analyzes the output of the GPT-2 117M language model from OpenAI, which allows it to rank each word according to how likely it is to have been produced by the model.

The tool then highlights the most likely words in green, followed by yellow and red, and the rest of the words in purple. GLTR provides a direct visual indication of how likely each word was under the model, making it easy to identify computer-generated text.

GLTR also shows three histograms which aggregate information over the whole text. The first histogram shows how many words of each category appear in the text, the second illustrates the ratio between the probabilities of the top predicted word and the following word, and the third shows the distribution over the entropies of the predictions.

By analyzing these histograms, GLTR provides additional evidence of whether a text has been artificially generated.GLTR can be used to detect fake reviews, comments, or news articles generated by large language models, which have the potential to produce texts that are indistinguishable from human-written text to a non-expert reader.

GLTR can be accessed through a live demo and the source code is available on Github. Researchers can also read the ACL 2019 demo track paper, which was nominated for best demo.

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GLTR was manually vetted by our editorial team and was first featured on April 10th 2023.
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Pros and Cons

Pros

Detects auto-generated text
Analyzes language model output
Visually ranks word likelihood
Uses color-coding
Shows three histograms
Detects fake reviews/comments/articles
Live demo access
Open source on Github
Demo track paper available
Forensic text analysis
Analyzes text prediction probabilities
Detects artificially written text
Overlay colored mask technique
Showcases top predicted words
Uncertainty analysis through histogram
Detects model-generated text
Identifies uncommon word usage
Enables academic text analysis
Analyzes scientific abstracts
Color-based visual footprint
Indicates text-writing entity

Cons

Limited scale detection
Requires advanced language knowledge
Assumes simple sampling scheme
Adversaries might alter sampling parameters
Potential for worse text generation
Cannot automatically detect large-scale abuse
Cannot handle complex and unexpected words
High uncertainty for human-written texts
Requires direct comparison with original model
Inability to detect adversarially tweaked texts

Q&A

What exactly is GLTR?
How does GLTR detect artificially generated text?
What is the significance of the color-coded words in GLTR's analysis?
What are the three histograms provided by GLTR and what do they represent?
What are some practical uses of GLTR?
Is the source code for GLTR available to the public?
How can GLTR be used to detect fake reviews or comments?
What is the GPT-2 117M language model that GLTR uses?
Can GLTR be used to differentiate between human-written text and machine-generated text?
How successful is GLTR at identifying computer-generated content?
What kind of visual evidence does GLTR provide?
Can the average user access a live demo of GLTR?
How is GLTR used in the field of natural language processing?
How can researchers use GLTR and where can they learn more?
What does it mean when GLTR highlights a word in purple?
Can GLTR be used to analyze news articles for authenticity?
Is there a paper or research study available that delves into the creation and use of GLTR?
What does it mean if most of the words in a text analyzed by GLTR are highlighted in green or yellow?
How does GLTR determine whether a word is likely to have been produced by a computer?
Can GLTR be used to detect all forms of artificially generated text or does it specialize in detecting specific types?

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