🔗 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»

1. A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future DirectionsComputational LinguisticsJunchao Wu, Shu Yang, Runzhe Zhan, et al.2025 · 277 citations
2. Aya Expanse: Combining Research Breakthroughs for a New Multilingual FrontierarXiv:2412.04261John Dang, Shivalika Singh, Daniel D'souza, et al.2024 · 184 citations
3. RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement LearningarXiv:2504.20073Zihan Wang, Kangrui Wang, Qineng Wang, et al.2025 · 71 citations
4. Do Large Language Models have an English Accent? Evaluating and Improving the Naturalness of Multilingual LLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Yanzhu Guo, Simone Conia, Zelin Zhou, et al.2025 · 25 citations
5. A Survey of Post-Training Scaling in Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Hanyu Lai, Xiao Liu, Junjie Gao, et al.2025 · 18 citations
6. MiniPLM: Knowledge Distillation for Pre-Training Language ModelsarXiv:2410.17215Yuxian Gu, Hao Zhou, Fandong Meng, et al.2024 · 12 citations
7. Sparse Logit Sampling: Accelerating Knowledge Distillation in LLMsAnnual Meeting of the Association for Computational Linguistics (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 generationConference of the European Chapter of the Association for Computational Linguistics (EACL)Thomas F Burns, Letitia Parcalabescu, Stephan Waeldchen, et al.2026 · 4 citations
9. Position: LLM Watermarking Should Align Stakeholders' Incentives for Practical AdoptionFindings of the Association for Computational LinguisticsYepeng 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 ModelsFindings of the Association for Computational LinguisticsKonrad Löhr, Shuzhou Yuan, Michael Färber2026 · 2 citations
11. Automatic Machine Translation Detection Using a Surrogate Multilingual Translation ModelTransactions of the Association for Computational Linguistics (TACL)Cristian García-Romero, Miquel Esplà-Gomis, Felipe Sánchez-Martínez2026 · 1 citation
12. Easy Samples Are All You Need: Self-Evolving LLMs via Data-Efficient Reinforcement LearningFindings of the Association for Computational LinguisticsZhiyin Yu, Bo Zhang, Qibin Hou, et al.2026 · 1 citation
13. Topic-Based Watermarks for Large Language ModelsFindings of the Association for Computational LinguisticsAlexander Nemecek, Yuzhou Jiang, Erman Ayday2026 · 1 citation
14. A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and SolutionsAnnual Meeting of the Association for Computational Linguistics (ACL)Zhiyin Yu, Yuchen Mou, Juncheng Yan, et al.2026
15. Data Pollination: An Emergent Ecological Process Driving AI Population EvolutionAnnual Meeting of the Association for Computational Linguistics (ACL)Shufang Xie, Qizhi Pei, Ang Lv, et al.2026
16. GhostWriter: Hidden AI-Generated Texts over Multiple Languages, Domains and GeneratorsLanguage Resources and Evaluation Conference (LREC)Manuel Schaaf, Kevin Bönisch, Alexander Mehler2026
17. Legal Considerations in the Use of Synthetic Data for AI Development and Finetuning: The Case of LLMs4EUJoint Workshop on Legal and Ethical Issues in Human Language Technologies and Computational Approaches to Language Data Pseudonymization, Anonymization, De-identification, and Data Privacy @ LRECKossay Talmoudi, Khalid Choukri, Amélie Gourgeot, et al.2026
18. Lunar-Bench: Towards Evaluating Task-Oriented Reasoning of LLMs in Lunar Exploration ScenariosFindings of the Association for Computational LinguisticsXin-Yu Xiao, Ye Tian, Erwei Yin, et al.2026
19. Observations and Remedies for Large Language Model Bias in Self-Consuming Performative LoopAnnual Meeting of the Association for Computational Linguistics (ACL)Yaxuan Wang, Zhongteng Cai, Yujia Bao, et al.2026
20. Repetition over Diversity: High-Signal Data Filtering for Sample-Efficient German Language ModelingarXiv:2604.28075Ansar Aynetdinov, Patrick Haller, Alan Akbik2026
21. Synthetic Eggs in Many Baskets: The Impact of Synthetic Data Diversity on LLM Fine-TuningFindings of the Association for Computational LinguisticsMax Schaffelder, Albert Gatt2026
22. Systematic Evaluation of the Quality of Synthetic Clinical Notes Rephrased by LLMs at Million-Note ScaleBioNLPJinghui Liu, Sarvesh Soni, Anthony Nguyen2026
23. Task-Aware LLM Routing with Multi-Level Task-Profile-Guided Data Synthesis for Cold-Start ScenariosAnnual Meeting of the Association for Computational Linguistics (ACL)Hui Liu, Bin Zou, Kecheng Chen, et al.2026
24. The Broken Telephone Changes Tone: Examining Nuanced Linguistic Cues in LLM Chains-of-TranslationWorkshop on Multilinguality in the Era of Large Language ModelsQuang Minh Nguyen, Maida Aizaz, Braahmi Padmakumar2026
25. The Generator-Eraser Paradox: Community Guidelines for Responsible LLM-Assisted Dialect Resource CreationWorkshop on Dialects in NLP –- A Resource PerspectiveWajdi Zaghouani2026

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

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/shumailov-2024-ai-models-collapse-trained-recursively/cited-by/