# Technical Report on the Pangram AI-Generated Text Classifier

arXiv: https://arxiv.org/abs/2402.14873 · HTML: https://mlsanity.com/paper/2402.14873/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 34 references that are in the map.
- First posted on arXiv 21 Feb 2024.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Hierarchical Neural Story Generation](https://mlsanity.com/paper/1805.04833/) (2018)
- [DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature](https://mlsanity.com/paper/2301.11305/) (2023)
- [Training Region-based Object Detectors with Online Hard Example Mining](https://mlsanity.com/paper/1604.03540/) (2016)
- [A Survey of Deep Active Learning](https://mlsanity.com/paper/2009.00236/) (2020)
- [GLTR: Statistical Detection and Visualization of Generated Text](https://mlsanity.com/paper/1906.04043/) (2019)
- [Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense](https://mlsanity.com/paper/2303.13408/) (2023)
- [XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages](https://mlsanity.com/paper/2106.13822/) (2021)
- [DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text](https://mlsanity.com/paper/2306.05540/) (2023)
- [The Multilingual Amazon Reviews Corpus](https://mlsanity.com/paper/2010.02573/) (2020)
- [A Question-Entailment Approach to Question Answering](https://mlsanity.com/paper/1901.08079/) (2019)
- [Testing of Detection Tools for AI-Generated Text](https://mlsanity.com/paper/2306.15666/) (2023)
- [Active Learning for Deep Detection Neural Networks](https://mlsanity.com/paper/1911.09168/) (2019)
- [Smaller Language Models are Better Black-box Machine-Generated Text Detectors](https://mlsanity.com/paper/2305.09859/) (2023)
- [Counter Turing Test CT^2: AI-Generated Text Detection is Not as Easy as You May Think -- Introducing AI Detectability Index](https://mlsanity.com/paper/2310.05030/) (2023)
- [On the Zero-Shot Generalization of Machine-Generated Text Detectors](https://mlsanity.com/paper/2310.05165/) (2023)

## Built on by (most cited first, 30 of 34)

- [Flipping the Dialogue: Training and Evaluating User Language Models](https://mlsanity.com/paper/2510.06552/) (2025)
- [People who frequently use ChatGPT for writing tasks are accurate and robust detectors of AI-generated text](https://mlsanity.com/paper/2501.15654/) (2025)
- [EditLens: Quantifying the Extent of AI Editing in Text](https://mlsanity.com/paper/2510.03154/) (2025)
- [AI-Slop to AI-Polish? Aligning Language Models through Edit-Based Writing Rewards and Test-time Computation](https://mlsanity.com/paper/2504.07532/) (2025)
- [DAMAGE: Detecting Adversarially Modified AI Generated Text](https://mlsanity.com/paper/2501.03437/) (2025)
- [GPTZero: Robust Detection of LLM-Generated Texts](https://mlsanity.com/paper/2602.13042/) (2026)
- [AI use in American newspapers is widespread, uneven, and rarely disclosed](https://mlsanity.com/paper/2510.18774/) (2025)
- [StoryScope: Investigating idiosyncrasies in AI fiction](https://mlsanity.com/paper/2604.03136/) (2026)
- [Frankentext: Stitching random text fragments into long-form narratives](https://mlsanity.com/paper/2505.18128/) (2025)
- [ESPERANTO: Evaluating Synthesized Phrases to Enhance Robustness in AI Detection for Text Origination](https://mlsanity.com/paper/2409.14285/) (2024)
- [Death of the Novel(ty): Beyond n-Gram Novelty as a Metric for Textual Creativity](https://mlsanity.com/paper/2509.22641/) (2025)
- [LLM-Generated or Human-Written? Comparing Review and Non-Review Papers on ArXiv](https://mlsanity.com/paper/2601.17036/) (2026)
- ['Your AI Text is not Mine': Redefining and Evaluating AI-generated Text Detection under Realistic Assumptions](https://mlsanity.com/paper/2606.04906/) (2026)
- [Gaming AI-Assisted Peer Reviews Poses New Risks to the Scientific Community](https://mlsanity.com/paper/2606.10159/) (2026)
- [Pangram 4 Technical Report](https://mlsanity.com/paper/2607.27183/) (2026)
- [Base Models Look Human To AI Detectors](https://mlsanity.com/paper/2605.19516/) (2026)
- [Black-Box Forensics for Conversational LLM Agents](https://mlsanity.com/paper/2606.22698/) (2026)
- [ExaGPT: Example-Based Machine-Generated Text Detection for Human Interpretability](https://mlsanity.com/paper/2502.11336/) (2025)
- [DoPE: Decoy Oriented Perturbation Encapsulation Human-Readable, AI-Hostile Documents for Academic Integrity](https://mlsanity.com/paper/2601.12505/) (2026)
- [Can You Make It Sound Like You? Post-Editing LLM-Generated Text for Personal Style](https://mlsanity.com/paper/2604.24444/) (2026)
- [Hitting a Moving Target: Test-Time Adaptation for AI Text Detection under Continual Distribution Shift](https://mlsanity.com/paper/2606.25152/) (2026)
- [AI translation of literary texts is "fine", but readers still prefer human translations](https://mlsanity.com/paper/2606.26040/) (2026)
- [Gram2Vec: An Interpretable Document Vectorizer](https://mlsanity.com/paper/2406.12131/) (2024)
- [Domain Gating Ensemble Networks for AI-Generated Text Detection](https://mlsanity.com/paper/2505.13855/) (2025)
- [Integrity Shield A System for Ethical AI Use & Authorship Transparency in Assessments](https://mlsanity.com/paper/2601.11093/) (2026)
- [Show, Don't TELL: Explainable AI-Generated Text Detection](https://mlsanity.com/paper/2605.27921/) (2026)
- [Measuring, Localizing, and Ablating Alignment Signatures in LLMs](https://mlsanity.com/paper/2605.30526/) (2026)
- [Non-Parametric Machine Text Detection via Multi-View Gaussian Processes](https://mlsanity.com/paper/2606.14060/) (2026)
- [Team DACTYL at PAN 2026: Bayesian Data Mixing and Empirical X-risk Minimization for AI-text Detection](https://mlsanity.com/paper/2607.17382/) (2026)
- [Stability-Aware Feature Design for Robust Watermark Detection in Machine-Generated Text](https://mlsanity.com/paper/2608.18102/) (2026)

## Abstract (arXiv)

We present Pangram Text, a transformer-based neural network trained to distinguish text written by large language models from text written by humans. Pangram Text outperforms zero-shot methods such as DetectGPT as well as leading commercial AI detection tools with over 38 times lower error rates on a comprehensive benchmark comprised of 10 text domains (student writing, creative writing, scientific writing, books, encyclopedias, news, email, scientific papers, short-form Q&A) and 8 open- and closed-source large language models. We propose a training algorithm, hard negative mining with synthetic mirrors, that enables our classifier to achieve orders of magnitude lower false positive rates on high-data domains such as reviews. Finally, we show that Pangram Text is not biased against nonnative English speakers and generalizes to domains and models unseen during training.