---
title: "Who cites AI models collapse when trained — 100 citing papers"
description: "100 papers of the Elamur Research library cite «AI models collapse when trained on recursively generated data», found in their bibliographies…"
canonical: "https://research.elamur.ai/free/en/papers/shumailov-2024-ai-models-collapse-trained-recursively/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/shumailov-2024-ai-models-collapse-trained-recursively/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [AI models collapse when trained on recursively…](https://md.research.elamur.ai/free/en/papers/shumailov-2024-ai-models-collapse-trained-recursively/) / cited by

# Who cites «AI models collapse when trained on recursively generated data»

100 papers of our library cite it (of the 149,439 whose bibliographies we read) · 25 with a page here, the most cited first · [← back to «AI models collapse when trained on recursively generated data»](https://md.research.elamur.ai/free/en/papers/shumailov-2024-ai-models-collapse-trained-recursively/)

1. **[A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions](https://md.research.elamur.ai/free/en/papers/wu-2025-survey-llm-generated-text-detection/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Junchao Wu, Shu Yang, Runzhe Zhan, et al.*2025 · 277 citations*
2. [**Aya Expanse: Combining Research Breakthroughs for a New Multilingual Frontier arXiv:2412.04261**John Dang, Shivalika Singh, Daniel D'souza, et al.*2024 · 184 citations*](https://md.research.elamur.ai/free/en/papers/dang-2024-aya-expanse-combining-research-breakthroughs/)
3. [**RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning arXiv:2504.20073**Zihan Wang, Kangrui Wang, Qineng Wang, et al.*2025 · 71 citations*](https://md.research.elamur.ai/free/en/papers/wang-2025-ragen-understanding-self-evolution-llm/)
4. **[Do Large Language Models have an English Accent? Evaluating and Improving the Naturalness of Multilingual LLMs](https://md.research.elamur.ai/free/en/papers/guo-2025-do-large-language-models-have/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yanzhu Guo, Simone Conia, Zelin Zhou, et al.*2025 · 25 citations*
5. **[A Survey of Post-Training Scaling in Large Language Models](https://md.research.elamur.ai/free/en/papers/lai-2025-survey-post-training-scaling-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hanyu Lai, Xiao Liu, Junjie Gao, et al.*2025 · 18 citations*
6. [**MiniPLM: Knowledge Distillation for Pre-Training Language Models arXiv:2410.17215**Yuxian Gu, Hao Zhou, Fandong Meng, et al.*2024 · 12 citations*](https://md.research.elamur.ai/free/en/papers/gu-2024-miniplm-knowledge-distillation-pre-training/)
7. **[Sparse Logit Sampling: Accelerating Knowledge Distillation in LLMs](https://md.research.elamur.ai/free/en/papers/anshumann-2025-sparse-logit-sampling-accelerating-knowledge/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Anshumann, Mohd Abbas Zaidi, Akhil Kedia, et al.*2025 · 6 citations*
8. **[Aleph-Alpha-GermanWeb: Improving German-language LLM pre-training with model-based data curation and synthetic data generation](https://md.research.elamur.ai/free/en/papers/burns-2026-aleph-alpha-germanweb-improving-german/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Thomas F Burns, Letitia Parcalabescu, Stephan Waeldchen, et al.*2026 · 4 citations*
9. **[Position: LLM Watermarking Should Align Stakeholders' Incentives for Practical Adoption](https://md.research.elamur.ai/free/en/papers/liu-2026-position-llm-watermarking-should-align/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yepeng Liu, Xuandong Zhao, Dawn Song, et al.*2026 · 2 citations*
10. **[The Hidden Bias: A Study on Explicit and Implicit Political Stereotypes in Large Language Models](https://md.research.elamur.ai/free/en/papers/lohr-2026-hidden-bias-study-explicit-implicit/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Konrad Löhr, Shuzhou Yuan, Michael Färber*2026 · 2 citations*
11. **[Automatic Machine Translation Detection Using a Surrogate Multilingual Translation Model](https://md.research.elamur.ai/free/en/papers/romero-2026-automatic-machine-translation-detection-surrogate/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Cristian García-Romero, Miquel Esplà-Gomis, Felipe Sánchez-Martínez*2026 · 1 citation*
12. **[Easy Samples Are All You Need: Self-Evolving LLMs via Data-Efficient Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/yu-2026-easy-samples-are-all-you/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhiyin Yu, Bo Zhang, Qibin Hou, et al.*2026 · 1 citation*
13. **[Topic-Based Watermarks for Large Language Models](https://md.research.elamur.ai/free/en/papers/nemecek-2026-topic-based-watermarks-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Alexander Nemecek, Yuzhou Jiang, Erman Ayday*2026 · 1 citation*
14. **[A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions](https://md.research.elamur.ai/free/en/papers/yu-2026-survey-reinforcement-learning-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhiyin Yu, Yuchen Mou, Juncheng Yan, et al.*2026*
15. **[Data Pollination: An Emergent Ecological Process Driving AI Population Evolution](https://md.research.elamur.ai/free/en/papers/xie-2026-data-pollination-emergent-ecological-process/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shufang Xie, Qizhi Pei, Ang Lv, et al.*2026*
16. **[GhostWriter: Hidden AI-Generated Texts over Multiple Languages, Domains and Generators](https://md.research.elamur.ai/free/en/papers/schaaf-2026-ghostwriter-hidden-ai-generated-texts/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Manuel Schaaf, Kevin Bönisch, Alexander Mehler*2026*
17. **[Legal Considerations in the Use of Synthetic Data for AI Development and Finetuning: The Case of LLMs4EU](https://md.research.elamur.ai/free/en/papers/talmoudi-2026-legal-considerations-use-synthetic-data/) [Joint Workshop on Legal and Ethical Issues in Human Language Technologies and Computational Approaches to Language Data Pseudonymization, Anonymization, De-identification, and Data Privacy @ LREC](https://md.research.elamur.ai/free/en/venues/legal/)**Kossay Talmoudi, Khalid Choukri, Amélie Gourgeot, et al.*2026*
18. **[Lunar-Bench: Towards Evaluating Task-Oriented Reasoning of LLMs in Lunar Exploration Scenarios](https://md.research.elamur.ai/free/en/papers/xiao-2026-lunar-bench-evaluating-task-oriented/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xin-Yu Xiao, Ye Tian, Erwei Yin, et al.*2026*
19. **[Observations and Remedies for Large Language Model Bias in Self-Consuming Performative Loop](https://md.research.elamur.ai/free/en/papers/wang-2026-observations-remedies-large-language-model/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yaxuan Wang, Zhongteng Cai, Yujia Bao, et al.*2026*
20. [**Repetition over Diversity: High-Signal Data Filtering for Sample-Efficient German Language Modeling arXiv:2604.28075**Ansar Aynetdinov, Patrick Haller, Alan Akbik*2026*](https://md.research.elamur.ai/free/en/papers/aynetdinov-2026-repetition-over-diversity-high-signal/)
21. **[Synthetic Eggs in Many Baskets: The Impact of Synthetic Data Diversity on LLM Fine-Tuning](https://md.research.elamur.ai/free/en/papers/schaffelder-2026-synthetic-eggs-many-baskets-impact/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Max Schaffelder, Albert Gatt*2026*
22. **[Systematic Evaluation of the Quality of Synthetic Clinical Notes Rephrased by LLMs at Million-Note Scale](https://md.research.elamur.ai/free/en/papers/liu-2026-systematic-evaluation-quality-synthetic-clinical/) [BioNLP](https://md.research.elamur.ai/free/en/venues/bionlp/)**Jinghui Liu, Sarvesh Soni, Anthony Nguyen*2026*
23. **[Task-Aware LLM Routing with Multi-Level Task-Profile-Guided Data Synthesis for Cold-Start Scenarios](https://md.research.elamur.ai/free/en/papers/liu-2026-task-aware-llm-routing-multi/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hui Liu, Bin Zou, Kecheng Chen, et al.*2026*
24. **[The Broken Telephone Changes Tone: Examining Nuanced Linguistic Cues in LLM Chains-of-Translation](https://md.research.elamur.ai/free/en/papers/nguyen-2026-broken-telephone-changes-tone-examining/) [Workshop on Multilinguality in the Era of Large Language Models](https://md.research.elamur.ai/free/en/venues/mellm/)**Quang Minh Nguyen, Maida Aizaz, Braahmi Padmakumar*2026*
25. **[The Generator-Eraser Paradox: Community Guidelines for Responsible LLM-Assisted Dialect Resource Creation](https://md.research.elamur.ai/free/en/papers/zaghouani-2026-generator-eraser-paradox-community-guidelines/) [Workshop on Dialects in NLP –- A Resource Perspective](https://md.research.elamur.ai/free/en/venues/dialres/)**Wajdi Zaghouani*2026*

75 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/shumailov-2024-ai-models-collapse-trained-recursively/) — AI models collapse when trained on recursively generated data