GLTR
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About
GLTR (Giant Language Model Test Room) is a visual forensic tool designed to help users detect text automatically generated by large language models. Developed through a collaboration between the MIT-IBM Watson AI Lab and HarvardNLP, the tool operates on the premise that computer-generated text often relies on the most statistically likely words at each position to deceive readers. In contrast, natural human writing frequently incorporates unpredictable or surprising word choices that remain contextually relevant. By exposing these underlying statistical patterns, GLTR provides a window into whether a piece of writing follows the mechanical logic of an AI or the creative variation of a human author. The tool functions by using a language model—specifically OpenAI’s GPT-2 117M—to analyze any given input text. It calculates the ranking of each word based on what the model would have predicted in that specific context. The interface then applies a color-coded mask over the text: green for words in the top 10 most likely predictions, yellow for the top 100, red for the top 1,000, and purple for everything else. This visual representation allows users to see at a glance if a text is too predictable to be human-written. Beyond the color-coding, GLTR provides detailed histograms showing the distribution of word ranks, probability ratios, and prediction entropy to offer a comprehensive statistical profile of the content. GLTR is primarily intended for researchers, journalists, and forensic analysts who need to verify the authenticity of digital content such as news articles, product reviews, or academic submissions. It is particularly useful for identifying text generated by models that use common sampling schemes, which often produce highly predictable results that GLTR excels at highlighting. While the tool is a powerful aid for non-experts—improving human detection rates of fake text from 54% to 72% in studies—it does require some linguistic intuition to determine if rare words highlighted in purple or red are used correctly or are simply errors. What distinguishes GLTR from many black-box AI detectors is its transparency and focus on human-AI collaboration. Instead of providing a single percentage score, it provides a deep dive into the specific linguistic artifacts of a text. This forensic approach allows users to investigate exactly why a passage feels artificial. Because the project is open-source and built on academic research presented at ACL 2019, it serves as both a functional utility and an educational resource for understanding the behavior of large language models.
Pros & Cons
Significantly improves human detection accuracy from 54% to 72%
Provides detailed statistical evidence instead of a single opaque score
Open-source code allows for local deployment and customization
Visual interface makes complex linguistic data accessible to non-experts
Backed by peer-reviewed research from MIT-IBM and HarvardNLP
Limited scale; primarily useful for individual case analysis rather than bulk scanning
Requires some human judgment to interpret if rare words make sense in context
May be less effective against adversarial sampling techniques designed to mimic human variation
Relies on a smaller version of GPT-2 (117M) which may miss nuances of larger modern models
Use Cases
Journalists can perform forensic analysis on suspicious news articles to verify if they were generated by a bot.
Academic researchers can use the visual footprints to study the distributional differences between human and machine writing styles.
Content moderators can screen suspicious product reviews by looking for the green visual footprint typical of high-probability AI text.
Students of linguistics can explore how language models predict sequences by interacting with the top-5 prediction tooltips.
Platform
Features
• interactive web demo
• gpt-2 117m integration
• prediction entropy distribution
• probability ratio histograms
• rank distribution histograms
• top 5 word prediction tooltips
• color-coded word ranking
• visual forensic analysis
FAQs
How does GLTR distinguish between AI and human text?
It analyzes how likely each word is according to a language model. AI text tends to consist mostly of highly predictable words (green and yellow), while human text contains more surprising or lower-ranked words (red and purple).
Can I see which specific words the model predicted?
Yes, by hovering over any word in the analyzed text, a box displays the top five predicted words, their associated probabilities, and the actual rank of the word used in the text.
Which AI models does GLTR use for detection?
The current implementation uses OpenAI's GPT-2 117M model to evaluate text. However, the methodology is designed so that it can be applied to other large language models.
Is GLTR effective for detecting text from any AI?
It is most effective against models using standard sampling schemes. If an adversary uses complex sampling to mimic human unpredictability, the text quality often degrades, creating other detectable artifacts.
Do I need to be a data scientist to use this tool?
No, the tool is designed for non-experts. In a human-subjects study, using GLTR improved the detection rate of fake text from 54% to 72% without any prior training.
Pricing Plans
Open Source / Demo
Free Plan• Live web demo access
• Visual color-coded overlays
• Statistical histograms
• Top 5 word predictions
• Open-source code on GitHub
• GPT-2 117M model analysis
Job Opportunities
There are currently no job postings for this AI tool.
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