mlsanityData to 14 Oct 2026

Distinguishing Human Generated Text From ChatGPT Generated Text Using Machine Learning

arXiv 2306.01761 ↗First posted 26 May 2023cs.CL cs.AI cs.LGcited by 10

Not on the map. Why?

Abstract (arXiv)

ChatGPT is a conversational artificial intelligence that is a member of the generative pre-trained transformer of the large language model family. This text generative model was fine-tuned by both supervised learning and reinforcement learning so that it can produce text documents that seem to be written by natural intelligence. Although there are numerous advantages of this generative model, it comes with some reasonable concerns as well. This paper presents a machine learning-based solution that can identify the ChatGPT delivered text from the human written text along with the comparative analysis of a total of 11 machine learning and deep learning algorithms in the classification process. We have tested the proposed model on a Kaggle dataset consisting of 10,000 texts out of which 5,204 texts were written by humans and collected from news and social media. On the corpus generated by GPT-3.5, the proposed algorithm presents an accuracy of 77%.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 16 of its 27 references that are in the map. First posted on arXiv 26 May 2023.

Builds on

16 of 27 references.

References · 16

  1. 2021CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classificationcited by 806
  2. 2023DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvaturecited by 726
  3. 2023A Watermark for Large Language Modelscited by 725
  4. 2019Release Strategies and the Social Impacts of Language Modelscited by 513
  5. 2019GLTR: Statistical Detection and Visualization of Generated Textcited by 496
  6. 2023Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defensecited by 429
  7. 2023Can AI-Generated Text be Reliably Detected?cited by 386
  8. 2023A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPTcited by 224
  9. 2021Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyondcited by 178
  10. 2020TweepFake: about Detecting Deepfake Tweetscited by 117
  11. 2017Natural Language Processing: State of The Art, Current Trends and Challengescited by 114
  12. 2023Regulating ChatGPT and other Large Generative AI Modelscited by 104
4 more
  1. 2019Real or Fake? Learning to Discriminate Machine from Human Generated Textcited by 99
  2. 2023On the Possibilities of AI-Generated Text Detectioncited by 88
  3. 2023ChatGPT or Human? Detect and Explain. Explaining Decisions of Machine Learning Model for Detecting Short ChatGPT-generated Textcited by 85
  4. 2021The NLP Cookbook: Modern Recipes for Transformer based Deep Learning Architecturescited by 16

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 7; 2025: 0; 2026: 1.

10 papers.