model collapse

CTRAP: Embedding Collapse Trap to Safeguard Large Language Models from Harmful Fine-Tuning ACL 2026 Data Pollination: An Emergent Ecological Process Driving AI Population Evolution ACL 2026 Demystifying Mixed Outcomes of Self-Training: Pre-training Analyses on Non-Toy LLMs EACL 2026 Learning from Synthetic Data: Limitations of ERM ALT 2026 Losing our Tail, Again: (Un)Natural Selection & Multilingual LLMs ACL 2026 Stabilizing Self-Consuming Diffusion Models with Latent Space Filtering AAAI 2026 A Theoretical Perspective: How to Prevent Model Collapse in Self-consuming Training Loops ICLR 2025 Beyond Model Collapse: Scaling Up with Synthesized Data Requires Verification ICLR 2025 Bias Amplification: Large Language Models as Increasingly Biased Media IJCNLP 2025 Collapse or Thrive: Perils and Promises of Synthetic Data in a Self-Generating World ICML 2025 COPR: Continual Human Preference Learning via Optimal Policy Regularization ACL 2025 Demystifying Synthetic Data in LLM Pre-training: A Systematic Study of Scaling Laws, Benefits, and Pitfalls EMNLP 2025 How to Synthesize Text Data without Model Collapse? ICML 2025 Machine-generated text detection prevents language model collapse EMNLP 2025 Ranked Entropy Minimization for Continual Test-Time Adaptation ICML 2025 Rate of Model Collapse in Recursive Training AISTATS 2025 Recursive Training Loops in LLMs: How training data properties modulate distribution shift in generated data? EMNLP 2025 Strong Model Collapse ICLR 2025 Superficial Self-Improved Reasoners Benefit from Model Merging EMNLP 2025 A Tale of Tails: Model Collapse as a Change of Scaling Laws ICML 2024 Collapse-Aware Triplet Decoupling for Adversarially Robust Image Retrieval ICML 2024 Dense Projection for Anomaly Detection AAAI 2024 Model Collapse Demystified: The Case of Regression NeurIPS 2024 Preventing Model Collapse in Deep Canonical Correlation Analysis by Noise Regularization NeurIPS 2024 Rebuilding ROME : Resolving Model Collapse during Sequential Model Editing EMNLP 2024