# GPT-who: An Information Density-based Machine-Generated Text Detector

arXiv: https://arxiv.org/abs/2310.06202 · HTML: https://mlsanity.com/paper/2310.06202/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 59 references that are in the map.
- First posted on arXiv 9 Oct 2023.

## Builds on (most cited first, 30 of 38)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Plug and Play Language Models: A Simple Approach to Controlled Text Generation](https://mlsanity.com/paper/1912.02164/) (2019)
- [The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only](https://mlsanity.com/paper/2306.01116/) (2023)
- [DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature](https://mlsanity.com/paper/2301.11305/) (2023)
- [Defending Against Neural Fake News](https://mlsanity.com/paper/1905.12616/) (2019)
- [Release Strategies and the Social Impacts of Language Models](https://mlsanity.com/paper/1908.09203/) (2019)
- [GLTR: Statistical Detection and Visualization of Generated Text](https://mlsanity.com/paper/1906.04043/) (2019)
- [Can AI-Generated Text be Reliably Detected?](https://mlsanity.com/paper/2303.11156/) (2023)
- [Toxicity in ChatGPT: Analyzing Persona-assigned Language Models](https://mlsanity.com/paper/2304.05335/) (2023)
- [DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text](https://mlsanity.com/paper/2306.05540/) (2023)
- [Facebook FAIR's WMT19 News Translation Task Submission](https://mlsanity.com/paper/1907.06616/) (2019)
- [Model evaluation for extreme risks](https://mlsanity.com/paper/2305.15324/) (2023)
- [Automatic Detection of Machine Generated Text: A Critical Survey](https://mlsanity.com/paper/2011.01314/) (2020)
- [TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation](https://mlsanity.com/paper/2109.13296/) (2021)
- [If beam search is the answer, what was the question?](https://mlsanity.com/paper/2010.02650/) (2020)
- [MGTBench: Benchmarking Machine-Generated Text Detection](https://mlsanity.com/paper/2303.14822/) (2023)
- [On the Possibilities of AI-Generated Text Detection](https://mlsanity.com/paper/2304.04736/) (2023)
- [Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods](https://mlsanity.com/paper/2210.07321/) (2022)
- [Do Language Models Plagiarize?](https://mlsanity.com/paper/2203.07618/) (2022)
- [Revisiting the Uniform Information Density Hypothesis](https://mlsanity.com/paper/2109.11635/) (2021)
- [Nationality Bias in Text Generation](https://mlsanity.com/paper/2302.02463/) (2023)
- [Artificial Text Detection via Examining the Topology of Attention Maps](https://mlsanity.com/paper/2109.04825/) (2021)
- [ArguGPT: evaluating, understanding and identifying argumentative essays generated by GPT models](https://mlsanity.com/paper/2304.07666/) (2023)
- [Neural Deepfake Detection with Factual Structure of Text](https://mlsanity.com/paper/2010.07475/) (2020)

## Built on by (most cited first, 30 of 40)

- [A Robust Semantics-based Watermark for Large Language Model against Paraphrasing](https://mlsanity.com/paper/2311.08721/) (2023)
- [Detecting Multimedia Generated by Large AI Models: A Survey](https://mlsanity.com/paper/2402.00045/) (2024)
- [A Survey on Detection of LLMs-Generated Content](https://mlsanity.com/paper/2310.15654/) (2023)
- [M4GT-Bench: Evaluation Benchmark for Black-Box Machine-Generated Text Detection](https://mlsanity.com/paper/2402.11175/) (2024)
- [GumbelSoft: Diversified Language Model Watermarking via the GumbelMax-trick](https://mlsanity.com/paper/2402.12948/) (2024)
- [SemEval-2024 Task 8: Multidomain, Multimodel and Multilingual Machine-Generated Text Detection](https://mlsanity.com/paper/2404.14183/) (2024)
- [Detecting Subtle Differences between Human and Model Languages Using Spectrum of Relative Likelihood](https://mlsanity.com/paper/2406.19874/) (2024)
- [A Ship of Theseus: Curious Cases of Paraphrasing in LLM-Generated Texts](https://mlsanity.com/paper/2311.08374/) (2023)
- [Detecting AI-Generated Text: Factors Influencing Detectability with Current Methods](https://mlsanity.com/paper/2406.15583/) (2024)
- [AI-generated text boundary detection with RoFT](https://mlsanity.com/paper/2311.08349/) (2023)
- [Authorship Obfuscation in Multilingual Machine-Generated Text Detection](https://mlsanity.com/paper/2401.07867/) (2024)
- [Robust AI-Generated Text Detection by Restricted Embeddings](https://mlsanity.com/paper/2410.08113/) (2024)
- [Does DetectGPT Fully Utilize Perturbation? Bridging Selective Perturbation to Fine-tuned Contrastive Learning Detector would be Better](https://mlsanity.com/paper/2402.00263/) (2024)
- [Is Contrasting All You Need? Contrastive Learning for the Detection and Attribution of AI-generated Text](https://mlsanity.com/paper/2407.09364/) (2024)
- [A Practical Examination of AI-Generated Text Detectors for Large Language Models](https://mlsanity.com/paper/2412.05139/) (2024)
- [GPTZero: Robust Detection of LLM-Generated Texts](https://mlsanity.com/paper/2602.13042/) (2026)
- [OpenTuringBench: An Open-Model-based Benchmark and Framework for Machine-Generated Text Detection and Attribution](https://mlsanity.com/paper/2504.11369/) (2025)
- [Authorship Attribution in Multilingual Machine-Generated Texts](https://mlsanity.com/paper/2508.01656/) (2025)
- [Human Texts Are Outliers: Detecting LLM-generated Texts via Out-of-distribution Detection](https://mlsanity.com/paper/2510.08602/) (2025)
- [Humanizing the Machine: Proxy Attacks to Mislead LLM Detectors](https://mlsanity.com/paper/2410.19230/) (2024)
- [Are You Human? An Adversarial Benchmark to Expose LLMs](https://mlsanity.com/paper/2410.09569/) (2024)
- [Using Machine Learning to Distinguish Human-written from Machine-generated Creative Fiction](https://mlsanity.com/paper/2412.15253/) (2024)
- [TM-TREK at SemEval-2024 Task 8: Towards LLM-Based Automatic Boundary Detection for Human-Machine Mixed Text](https://mlsanity.com/paper/2404.00899/) (2024)
- [AISPACE at SemEval-2024 task 8: A Class-balanced Soft-voting System for Detecting Multi-generator Machine-generated Text](https://mlsanity.com/paper/2404.00950/) (2024)
- [Beyond checkmate: exploring the creative chokepoints in AI text](https://mlsanity.com/paper/2501.19301/) (2025)
- [Kill two birds with one stone: generalized and robust AI-generated text detection via dynamic perturbations](https://mlsanity.com/paper/2504.21019/) (2025)
- [AICD Bench: A Challenging Benchmark for AI-Generated Code Detection](https://mlsanity.com/paper/2602.02079/) (2026)
- [Spotlights and Blindspots: Evaluating Machine-Generated Text Detection](https://mlsanity.com/paper/2604.16607/) (2026)
- [Representational Similarity and Model Behavior in Multi-Agent Interaction](https://mlsanity.com/paper/2606.07818/) (2026)
- [Black-Box Forensics for Conversational LLM Agents](https://mlsanity.com/paper/2606.22698/) (2026)

## Abstract (arXiv)

The Uniform Information Density (UID) principle posits that humans prefer to spread information evenly during language production. We examine if this UID principle can help capture differences between Large Language Models (LLMs)-generated and human-generated texts. We propose GPT-who, the first psycholinguistically-inspired domain-agnostic statistical detector. This detector employs UID-based features to model the unique statistical signature of each LLM and human author for accurate detection. We evaluate our method using 4 large-scale benchmark datasets and find that GPT-who outperforms state-of-the-art detectors (both statistical- & non-statistical) such as GLTR, GPTZero, DetectGPT, OpenAI detector, and ZeroGPT by over 20% across domains. In addition to better performance, it is computationally inexpensive and utilizes an interpretable representation of text articles. We find that GPT-who can distinguish texts generated by very sophisticated LLMs, even when the overlying text is indiscernible. UID-based measures for all datasets and code are available at https://github.com/saranya-venkatraman/gpt-who.