GLTR
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About
GLTR, the Giant Language Model Test Room, is a tool to detect automatically generated text by inspecting its visual footprint. It operates on the assumption that computer-generated text often sticks to the most probable words, unlike human-written text which uses more unpredictable yet contextually appropriate words. GLTR utilizes the GPT-2 117M language model to analyze text input, predicting what GPT-2 would have generated at each position. It overlays a colored mask on the text: green (top 10 most likely words), yellow (top 100), red (top 1,000), and purple (rest). This visual cue helps identify patterns indicative of AI generation. Users can hover over words to see top 5 predictions and probabilities. The tool also provides histograms for word category counts, probability ratios, and prediction entropies, offering further insights. GLTR has been shown to significantly improve human detection rates of fake text, raising it from 54% to 72% without prior training. While not suitable for large-scale abuse detection, it serves as a valuable forensic tool for individual cases. It is an open-source collaboration between MIT-IBM Watson AI lab and HarvardNLP.
Platform
Task
Features
• open-source and publicly deployed
• proven to improve human detection rates of fake text
• offers aggregate histograms for text analysis
• displays top 5 predicted words and probabilities
• highlights words based on prediction likelihood with colored masks
• integrates with gpt-2 117m language model
• provides visual footprint analysis
• detects automatically generated text
FAQs
What is GLTR?
GLTR (Giant Language Model Test Room) is a visually forensic tool designed to help humans detect text that was automatically generated by large language models.
How does GLTR detect generated text?
It analyzes text by checking how likely each word was under a language model (like GPT-2), highlighting words based on their prediction rank.
What do the colors in GLTR mean?
Green indicates words in the top 10 most likely predictions, yellow for top 100, red for top 1,000, and purple for words outside the top 1,000.
Can GLTR detect human-written text?
Yes, it identifies human-written text by a higher frequency of red and purple words, as natural writing uses more unpredictable terms.
Is GLTR able to detect all types of generated text?
GLTR is limited in scale and assumes simple sampling schemes. It works best for individual cases and may be less effective against highly adversarial generation.
Is GLTR open-source?
Yes, GLTR is open-source, and its source code is publicly available on GitHub.
Who developed GLTR?
GLTR was developed by Hendrik Strobelt and Sebastian Gehrmann, in collaboration with MIT-IBM Watson AI lab and HarvardNLP.
Pricing Plans
Free
Free Plan• Visual forensic analysis
• Detects automatically generated text
• GPT-2 117M model integration
• Colored word highlighting
• Top 5 word predictions
• Histograms for text analysis
• Open-source access
Job Opportunities
There are currently no job postings for this AI tool.
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