mlsanityData to 7 Oct 2026

Towards Understanding the Interplay of Generative Artificial Intelligence and the Internet

arXiv 2306.06130 ↗First posted 8 Jun 2023cs.AI cs.CV cs.LGcited by 54

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Abstract (arXiv)

The rapid adoption of generative Artificial Intelligence (AI) tools that can generate realistic images or text, such as DALL-E, MidJourney, or ChatGPT, have put the societal impacts of these technologies at the center of public debate. These tools are possible due to the massive amount of data (text and images) that is publicly available through the Internet. At the same time, these generative AI tools become content creators that are already contributing to the data that is available to train future models. Therefore, future versions of generative AI tools will be trained with a mix of human-created and AI-generated content, causing a potential feedback loop between generative AI and public data repositories. This interaction raises many questions: how will future versions of generative AI tools behave when trained on a mixture of real and AI generated data? Will they evolve and improve with the new data sets or on the contrary will they degrade? Will evolution introduce biases or reduce diversity in subsequent generations of generative AI tools? What are the societal implications of the possible degradation of these models? Can we mitigate the effects of this feedback loop? In this document, we explore the effect of this interaction and report some initial results using simple diffusion models trained with various image datasets. Our results show that the quality and diversity of the generated images can degrade over time suggesting that incorporating AI-created data can have undesired effects on future versions of generative models.

Key facts. Cited by 54 papers in the map (data through 7 Oct 2026). Builds on 22 of its 37 references that are in the map. First posted on arXiv 8 Jun 2023.

Builds on

22 of 37 references.

References · 22

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2020Denoising Diffusion Implicit Modelscited by 9,655
  3. 2022Classifier-Free Diffusion Guidancecited by 5,717
  4. 2014Going Deeper with Convolutionscited by 4,231
  5. 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,131
  6. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  7. 2021LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairscited by 1,544
  8. 2019Improved Precision and Recall Metric for Assessing Generative Modelscited by 925
  9. 2021Datasets: A Community Library for Natural Language Processingcited by 526
  10. 2021Tackling the Generative Learning Trilemma with Denoising Diffusion GANscited by 515
  11. 2020Reliable Fidelity and Diversity Metrics for Generative Modelscited by 425
  12. 2023The Curse of Recursion: Training on Generated Data Makes Models Forgetcited by 343
10 more
  1. 2023Synthetic Data from Diffusion Models Improves ImageNet Classificationcited by 326
  2. 2023Text-to-image Diffusion Models in Generative AI: A Surveycited by 264
  3. 2023The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Datasetcited by 148
  4. 2023Intriguing properties of synthetic images: from generative adversarial networks to diffusion modelscited by 121
  5. 2020Feedback Loop and Bias Amplification in Recommender Systemscited by 89
  6. 2023ChatGPT is not all you need. A State of the Art Review of large Generative AI modelscited by 85
  7. 2023Evading Watermark based Detection of AI-Generated Contentcited by 74
  8. 2019Degenerate Feedback Loops in Recommender Systemscited by 67
  9. 2022Will Large-scale Generative Models Corrupt Future Datasets?cited by 59
  10. 2023Combining Generative Artificial Intelligence (AI) and the Internet: Heading towards Evolution or Degradation?cited by 41

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 9; 2024: 24; 2025: 15; 2026: 6.

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  1. 2023Self-Consuming Generative Models Go MADcited by 239
  2. 2024Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Datacited by 143
  3. 2024A Tale of Tails: Model Collapse as a Change of Scaling Lawscited by 127
  4. 2023On the Stability of Iterative Retraining of Generative Models on their own Datacited by 98
  5. 2024Understanding Hallucinations in Diffusion Models through Mode Interpolationcited by 87
  6. 2024Model Collapse Demystified: The Case of Regressioncited by 80
  7. 2023Large Language Models Suffer From Their Own Output: An Analysis of the Self-Consuming Training Loopcited by 75
  8. 2024Strong Model Collapsecited by 62
  9. 2024Fairness Feedback Loops: Training on Synthetic Data Amplifies Biascited by 55
  10. 2024Collapse or Thrive? Perils and Promises of Synthetic Data in a Self-Generating Worldcited by 49
  11. 2025The Best Instruction-Tuning Data are Those That Fitcited by 48
  12. 2024Beyond Model Collapse: Scaling Up with Synthesized Data Requires Verificationcited by 43
38 more
  1. 2024Self-Correcting Self-Consuming Loops for Generative Model Trainingcited by 34
  2. 2025Position: Model Collapse Does Not Mean What You Thinkcited by 32
  3. 2024Towards Theoretical Understandings of Self-Consuming Generative Modelscited by 28
  4. 2023Human-AI Coevolutioncited by 27
  5. 2023Nepotistically Trained Generative-AI Models Collapsecited by 27
  6. 2024Universality of the π2/6 Pathway in Avoiding Model Collapsecited by 24
  7. 2024How to Synthesize Text Data without Model Collapse?cited by 24
  8. 2024Rate of Model Collapse in Recursive Trainingcited by 22
  9. 2024Heat Death of Generative Models in Closed-Loop Learningcited by 21
  10. 2024Real Risks of Fake Data: Synthetic Data, Diversity-Washing and Consent Circumventioncited by 21
  11. 2025A Theoretical Perspective: How to Prevent Model Collapse in Self-consuming Training Loopscited by 15
  12. 2024Step-On-Feet Tuning: Scaling Self-Alignment of LLMs via Bootstrappingcited by 14
  13. 2024Model Collapse in the Self-Consuming Chain of Diffusion Finetuning: A Novel Perspective from Quantitative Trait Modelingcited by 11
  14. 2024Beware of Words: Evaluating the Lexical Diversity of Conversational LLMs using ChatGPT as Case Studycited by 10
  15. 2023Playing with words: Comparing the vocabulary and lexical diversity of ChatGPT and humanscited by 8
  16. 2025Why Less is More (Sometimes): A Theory of Data Curationcited by 8
  17. 2023Hazards from Increasingly Accessible Fine-Tuning of Downloadable Foundation Modelscited by 7
  18. 2024Generative AI and Copyright: A Dynamic Perspectivecited by 7
  19. 2025Preventing Model Collapse Under Overparametrization: Optimal Mixing Ratios for Interpolation Learning and Ridge Regressioncited by 7
  20. 2023How many words does ChatGPT know? The answer is ChatWordscited by 6
  21. 2024Sociotechnical Implications of Generative Artificial Intelligence for Information Accesscited by 6
  22. 2025How Large Language Models Are Changing MOOC Essay Answers: A Comparison of Pre- and Post-LLM Responsescited by 5
  23. 2025What happens when generative AI models train recursively on each others' outputs?cited by 5
  24. 2026Language Generation with Replay: A Learning-Theoretic View of Model Collapsecited by 5
  25. 2024When AI Eats Itself: On the Caveats of AI Autophagycited by 4
  26. 2024Dealing with Synthetic Data Contamination in Online Continual Learningcited by 4
  27. 2024Recursive InPainting (RIP): how much information is lost under recursive inferences?cited by 2
  28. 2024Learning by Surprise: Adaptive Mitigation of Model Collapse in Large Language Modelscited by 2
  29. 2025Did ChatGPT or Copilot use alter the style of internet news headlines? A time series regression analysiscited by 2
  30. 2025Recursive Training Loops in LLMs: How training data properties modulate distribution shift in generated data?cited by 2
  31. 2025Information Retrieval in the Age of Generative AI: The RGB Modelcited by 2
  32. 2025Review of Hallucination Understanding in Large Language and Vision Modelscited by 2
  33. 2025Compliance Rating Scheme: A Data Provenance Framework for Generative AI Datasetscited by 2
  34. 2026Ambient Dataloops: Generative Models for Dataset Refinementcited by 2
  35. 2023Establishing Vocabulary Tests as a Benchmark for Evaluating Large Language Modelscited by 1
  36. 2025Convergence Dynamics and Stabilization Strategies of Co-Evolving Generative Modelscited by 1
  37. 2025Losing our Tail, Again: (Un)Natural Selection & Multilingual LLMscited by 1
  38. 2025Deep Generative Continual Learning using Functional LoRA: FunLoRAcited by 0

50 most cited of 54 All 54 (JSON)

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  1. 2023Exploring the Potential of Generative AI for the World Wide Web
  2. 2025If generative AI is the answer, what is the question?
  3. 2024Can Prompt Modifiers Control Bias? A Comparative Analysis of Text-to-Image Generative Models
  4. 2025Delving into: the quantification of Ai-generated content on the internet (synthetic data)
  5. 2024Synthetic Image Generation in Cyber Influence Operations: An Emergent Threat?
  6. 2024Generative AI for Synthetic Data Generation: Methods, Challenges and the Future
  7. 2026Position: the Stochastic Parrot in the Coal Mine. Model Collapse is a Threat to Low-Resource Communities
  8. 2025Foundations of GenIR