How AI Detection at GPTZero works
GPTZero’s technology uses deep learning to keep pace with AI advancements to deliver precise, reliable results that help you understand and interpret the origin of a piece of text.
Input Text
GPTZero accepts copy and pasted text, docx, pdf, and image files, analyzing up to 50 files at a time.
Deep Learning
We employ an end-to-end deep learning approach, trained on text datasets from the web, education, and AI- generated from a range of LLMs.
Sentence Classifier
A sentence-by-sentence classification model determines the probability and confidence that a text was created by AI.
Paraphraser Shield
We defend against tools looking to exploit AI detectors. Our model shields against common methods to bypass AI detection, such as paraphrasing and homoglyph attacks.
Output Result
You can view easy-to-interpret results in our dashboard, with premium features to detect AI vocabulary, plagiarism, and citeable sources.
Leading the way in AI Detection Research
It is becoming increasingly critical to develop robust tools to detect AI-generated texts and limit the adverse effects of LLMs. GPTZero’s mission is to ensure that human-authored and LLM-generated text remains distinguishable. We achieve this goal by offering a commercially available AI detector that is highly accurate, scalable, and – most importantly – capable of delivering explainable predictions that allow users to responsibly interpret the results.
Our wider research contributions include:
We frame the LLM-generated text detection as a trinary classification problem, separating prediction confidence from the proportion of LLM text.
We developed the first sentence highlighting model using HMM (Hidden Markov Models) for areas of text, featured on Anderson Cooper 360.
We developed a novel output mapping mechanism which improves model calibration and biases the detector to prefer making less-harmful false-negative errors over false-positive errors.
We continuously demonstrate superior AI detection performance against both commercial and open-source alternatives across multiple genres and languages.
We outlined an industrial-scale framework for collecting and cleaning data, training and utilizing supervised-models, and considerations on user interaction with the models.
Cyclical Development Process of our Deep Learning Model
De-biasing Detection for Education
Our team is dedicated to de-biasing our AI classification models for educational use cases.
For example, our efforts in reducing ESL bias in classification since April 2022 have reduced AI detection’s false positive rate on TOEFL texts to 1.1%.
We achieved our successful de-biasing via several methods, including model parameter tagging that incorporated an “education” tag in model training, text preclassification at the model output step, and representative dataset insertions. Through training a classification model, we can predict beforehand whether a text is likely from an ESL writer, to ensure the AI identification model has this information when making a classification.
Confidence Scores
We were the first detector to provide confidence categories for our classifications: “uncertain,” “moderately confident,” and “highly confident.” These categories are tuned so that the average error rate is less than 1% for the “high” confidence predictions, based on a diverse evaluation dataset used internally that was never before seen by the model.
Average error rate is emphasized because the number of possible documents is vast, varying substantially in tone, content, length, grammatical correctness, logical coherence, and structure.
Mixed Classification
GPTZero was the first detector to include a classification of “mixed” human and AI content. Our model outputs 3 possible classifications instead of the normal binary (human vs. AI):
- written entirely by a human
- written entirely by an AI
- written by a mix of human and AI
This allows for a more nuanced AI detection result.
Our Approach to Benchmarking
We are strongly supportive of the work of independent and academic reviewers in evaluating the progress of AI models.
We provide free API access to our model upon request for academic researchers. We’ve been evaluated by researchers from MIT, Harvard, Stanford, and several other universities.
From internal and external benchmarking, we find GPTZero is much better than our competitors at detecting mixed documents where both AI and human writing is involved, with a 96.5% accuracy rate.
False Positives
A false positive in AI detection is when an AI detector incorrectly classifies a human’s writing as AI. If, for instance, you are an educator or an institution that relies on AI detection tools to help inform your disciplinary policy around students’ AI usage, you will want to make sure the false positive rate is as low as possible to avoid false claims of cheating. We keep GPTZero’s false positive rate at no more than 1% when evaluating AI versus human text.
Researchers using GPTZero for AI detection
Join your fellow researchers using GPTZero for their papers, publications, and investigations.
Giuseppe Russo Latona, Manoel Horta Ribeiro, Tim R. Davidson, Veniamin Veselovsky, Robert West
"We estimate that 15.8% of ICLR reviews in 2024 were crafted with the assistance of an LLM, or 4,428 of the 28,028 reviews submitted that year; 49.4% of all submissions received at least one review classified as AI-assisted by GPTZero."
Pablo Picazo-Sanchez & Lara Ortiz-Martin
"In other words, no matter which editorial the analysed text comes from, the detector with the highest accuracy is GPTZero."
Frederick M. Howard, Anran Li, Mark F. Riffon, Elizabeth Garrett-Mayer, and Alexander T. Pearson
"GPTZero had the best discrimination of the pure AI-generated abstracts at an optimal threshold selected with Youden’s index, identifying 99.5% of AI-written abstracts with no false positives among human-written text. AI, artificial intelligence."
Creston Brooks, Samuel Eggert, Denis Peskoff
"Using two tools, GPTZero and Binoculars, we detect that as many as 5% of 2,909 English Wikipedia articles created in August 2024 contain significant AI-generated content."
Other papers using GPTZero
General FAQs about our AI Detector
Everything you need to know about GPTZero and our chat gpt detector.
Can’t find an answer? You can talk to our customer service team.
How does GPTZero detect AI-generated text?
GPTZero uses deep learning models that keep pace with AI advancements to deliver precise, reliable results that help you understand and interpret the origin of a piece of text. A sentence-by-sentence classifier calculates the probability that a text was created by AI, giving you both document-level and granular insights.
How accurate is GPTZero at detecting AI text?
Independent and internal benchmarking show that GPTZero outperforms competitors at detecting mixed documents (where both AI and human writing has been used) with 96.5% accuracy. Our false positive rate is under 1%, making it one of the most reliable detectors available.
Can GPTZero detect text from the latest AI models?
Yes. GPTZero is continually updated to recognize text from the newest large language models, including ChatGPT (GPT-3, GPT-4, GPT-5), Google Gemini, LLaMA, Claude, and others. As new models are released, we adapt and update our detection system.
How does GPTZero reduce false positives, especially for ESL students?
Since 2022, we've focused on reducing bias for ESL (English as a Second Language) writers. By tagging educational data, adding representative datasets, and using text pre-classification, we've reduced the false positive rate on TOEFL essays to just 1.1%.
How does GPTZero handle paraphrased or modified AI content?
Many try to bypass AI detectors by paraphrasing or using homoglyph substitutions. Our model's Paraphraser Shield technology is designed to catch these cases. Even if AI content has been altered to look more human-like, GPTZero can detect it.
What are the known limitations of AI detection?
No AI detector is 100% accurate, and AI itself is changing constantly. GPTZero performs best of longer texts and English prose. We encourage using it as a conversation starter, and not as the final verdict. GPTZero is the only detector specifically de-biased for ESL writers, lowering false positives to 1%.
What data did you train your model on?
Our model is trained on millions of documents spanning various domains of writing including creating writing, scientific writing, blogs, news articles, and more. We test our models on a never-before-seen set of human and AI articles from a section of our large-scale dataset, in addition to a smaller set of challenging articles that are outside its training distribution.
How do I use and interpret the results from your API?
When you run text through our API, you'll see a document_classification field that labels it as HUMAN_ONLY, MIXED, and AI_ONLY. Each classification comes with probabilities (via the class_probabilities field) and a confidence_category field, which can be high, medium, or low. When the confidence is "high", error rates are below 1%. The API provides sentence-level highlights (API users can access this highlighting through the highlight_sentence_for_ai field) so you can see exactly which parts of the text contributed most to the classification.
Are you storing data from API calls?
No. We do not store or collect the documents passed into any calls to our API. For dashboard users, only aggregate inputs are stored to improve the service. See our privacy policy for full details.