Model collapse, and the shrinking diversity of how we write

What generated text does to models and to people: collapse, curation, and lost diversity.

1. Matthias Gerstgrasser, Rylan Schaeffer, Apratim Dey, Rafael Rafailov, Dhruv Pai, Henry Sleight, John Hughes, Tomasz Korbak, Rajashree Agrawal, Andrey Gromov, Daniel A. Roberts, Diyi Yang, David Donoho, and Sanmi Koyejo, "Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data," arXiv:2404.01413 (2024). https://arxiv.org/abs/2404.01413
2. Xuekai Zhu, Daixuan Cheng, Hengli Li, Kaiyan Zhang, Ermo Hua, Xingtai Lv, Ning Ding, Zhouhan Lin, Zilong Zheng, and Bowen Zhou, "How to Synthesize Text Data without Model Collapse?" arXiv:2412.14689 (2024). https://arxiv.org/abs/2412.14689
3. Ali Falahati, Mohammad Mohammadi Amiri, Kate Larson, and Lukasz Golab, "Curated Synthetic Data Doesn't Have to Collapse: A Theoretical Study of Generative Retraining with Pluralistic Preferences," arXiv:2605.07724 (2026). https://arxiv.org/abs/2605.07724
4. Zhivar Sourati, Farzan Karimi-Malekabadi, Meltem Ozcan, Colin McDaniel, Alireza Ziabari, Jackson Trager, Ala N. Tak, Meng Chen, Fred Morstatter, and Morteza Dehghani, "The Shrinking Landscape of Linguistic Diversity in the Age of Large Language Models," arXiv:2502.11266 (2025). https://arxiv.org/abs/2502.11266