{"total":44,"rows":[{"id":"2406.20094","title":"Scaling Synthetic Data Creation with 1,000,000,000 Personas","created":"2024-06-28","cited_by_count":417,"area":"synth"},{"id":"2402.07712","title":"Model Collapse Demystified: The Case of Regression","created":"2024-02-12","cited_by_count":82,"area":"synth"},{"id":"2404.05090","title":"How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse","created":"2024-04-07","cited_by_count":79,"area":"synth"},{"id":"2410.04840","title":"Strong Model Collapse","created":"2024-10-07","cited_by_count":65,"area":"synth"},{"id":"2406.07515","title":"Beyond Model Collapse: Scaling Up with Synthesized Data Requires Verification","created":"2024-06-11","cited_by_count":45,"area":"synth"},{"id":"2503.03150","title":"Position: Model Collapse Does Not Mean What You Think","created":"2025-03-05","cited_by_count":33,"area":"synth"},{"id":"2502.01612","title":"Self-Improving Transformers Overcome Easy-to-Hard and Length Generalization Challenges","created":"2025-02-03","cited_by_count":31,"area":"synth"},{"id":"2510.01631","title":"Demystifying Synthetic Data in LLM Pre-training: A Systematic Study of Scaling Laws, Benefits, and Pitfalls","created":"2025-10-02","cited_by_count":26,"area":"synth"},{"id":"2412.14689","title":"How to Synthesize Text Data without Model Collapse?","created":"2024-12-19","cited_by_count":25,"area":"synth"},{"id":"2412.17646","title":"Rate of Model Collapse in Recursive Training","created":"2024-12-23","cited_by_count":25,"area":"synth"},{"id":"2502.18865","title":"A Theoretical Perspective: How to Prevent Model Collapse in Self-consuming Training Loops","created":"2025-02-26","cited_by_count":16,"area":"synth"},{"id":"2502.15654","title":"Machine-generated text detection prevents language model collapse","created":"2025-02-21","cited_by_count":14,"area":"synth"},{"id":"2411.00750","title":"Mitigating Tail Narrowing in LLM Self-Improvement via Socratic-Guided Sampling","created":"2024-11-01","cited_by_count":13,"area":"synth"},{"id":"2505.19046","title":"When Models Don't Collapse: On the Consistency of Iterative MLE","created":"2025-05-25","cited_by_count":13,"area":"synth"},{"id":"2407.17493","title":"Model Collapse in the Self-Consuming Chain of Diffusion Finetuning: A Novel Perspective from Quantitative Trait Modeling","created":"2024-07-04","cited_by_count":12,"area":"synth"},{"id":"2410.15234","title":"Bias Amplification: Large Language Models as Increasingly Biased Media","created":"2024-10-19","cited_by_count":12,"area":"synth"},{"id":"2502.18049","title":"Recursive Learning Without Collapse: A Weighting-Based Stabilization Framework","created":"2025-02-25","cited_by_count":10,"area":"synth"},{"id":"2509.22341","title":"Preventing Model Collapse Under Overparametrization: Optimal Mixing Ratios for Interpolation Learning and Ridge Regression","created":"2025-09-26","cited_by_count":9,"area":"synth"},{"id":"2510.16657","title":"Escaping Model Collapse via Synthetic Data Verification: Near-term Improvements and Long-term Convergence","created":"2025-10-18","cited_by_count":6,"area":"synth"},{"id":"2603.11784","title":"Language Generation with Replay: A Learning-Theoretic View of Model Collapse","created":"2026-03-12","cited_by_count":5,"area":"synth"},{"id":"2603.25681","title":"Self-Improvement of Large Language Models: A Technical Overview and Future Outlook","created":"2026-03-26","cited_by_count":5,"area":"synth"},{"id":"2501.00747","title":"DIVE: Diversified Iterative Self-Improvement","created":"2025-01-01","cited_by_count":3,"area":"synth"},{"id":"2410.12341","title":"Learning by Surprise: Adaptive Mitigation of Model Collapse in Large Language Models","created":"2024-10-16","cited_by_count":2,"area":"synth"},{"id":"2501.18962","title":"Spend Wisely: Maximizing Post-Training Gains in Iterative Synthetic Data Bootstrapping","created":"2025-01-31","cited_by_count":2,"area":"synth"},{"id":"2503.08117","title":"Convergence Dynamics and Stabilization Strategies of Co-Evolving Generative Models","created":"2025-03-11","cited_by_count":2,"area":"synth"},{"id":"2606.28438","title":"When AI Reviews Its Own Code: Recursive Self-Training Collapse in Code LLMs","created":"2026-06-26","cited_by_count":2,"area":"synth"},{"id":"2506.19262","title":"What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning","created":"2025-06-24","cited_by_count":1,"area":"synth"},{"id":"2509.04796","title":"Knowledge Collapse in LLMs: When Fluency Survives but Facts Fail under Recursive Synthetic Training","created":"2025-09-05","cited_by_count":1,"area":"synth"},{"id":"2509.08972","title":"ForTIFAI: Fending Off Recursive Training Induced Failure for AI Model Collapse","created":"2025-09-10","cited_by_count":1,"area":"synth"},{"id":"2511.01490","title":"Synthetic Eggs in Many Baskets: The Impact of Synthetic Data Diversity on LLM Fine-Tuning","created":"2025-11-03","cited_by_count":1,"area":"synth"},{"id":"2511.13640","title":"Data Value in the Age of Scaling: Understanding LLM Scaling Dynamics Under Real-Synthetic Data Mixtures","created":"2025-11-17","cited_by_count":1,"area":"synth"},{"id":"2602.10531","title":"From Collapse to Improvement: Statistical Perspectives on the Evolutionary Dynamics of Iterative Training on Contaminated Sources","created":"2026-02-11","cited_by_count":1,"area":"synth"},{"id":"2605.04127","title":"Position: the Stochastic Parrot in the Coal Mine. Model Collapse is a Threat to Low-Resource Communities","created":"2026-05-05","cited_by_count":1,"area":"synth"},{"id":"2605.23054","title":"Model Collapse as Cultural Evolution","created":"2026-05-21","cited_by_count":1,"area":"synth"},{"id":"2607.17043","title":"Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning","created":"2026-07-19","cited_by_count":1,"area":"synth"},{"id":"2602.19033","title":"A Markovian View of Iterative-Feedback Loops in Image Generative Models: Neural Resonance and Model Collapse","created":"2026-02-22","cited_by_count":0,"area":"synth"},{"id":"2605.16379","title":"An Information-Theoretic Criterion for Efficient Data Synthesis","created":"2026-05-11","cited_by_count":0,"area":"synth"},{"id":"2605.20279","title":"The Economics of Model Collapse: Equilibrium, Welfare, and Optimal Provenance Subsidies in Synthetic Data Markets","created":"2026-05-19","cited_by_count":0,"area":"synth"},{"id":"2605.20602","title":"Self-Training Doesn't Flatten Language -- It Restructures It: Surface Markers Amplify While Deep Syntax Dies","created":"2026-05-20","cited_by_count":0,"area":"synth"},{"id":"2608.04268","title":"The Fairness Collapse Phenomenon: Bias Amplification in Language Models Trained on Synthetic Data","created":"2026-08-04","cited_by_count":0,"area":"synth"},{"id":"2608.21366","title":"Reviewing Model Collapse and Countermeasures","created":"2026-06-17","cited_by_count":0,"area":"synth"},{"id":"2609.09572","title":"Learning with Synthetic Data via SGD in High-Dimensional Linear Regression","created":"2026-09-09","cited_by_count":0,"area":"synth"},{"id":"2609.11146","title":"The Oligarch Barely Steers Model Collapse in Multi-Model Ecosystems","created":"2026-09-10","cited_by_count":0,"area":"synth"},{"id":"2609.12353","title":"SynthSentry: Detecting Synthetic Data Contamination in Language Model Training Data","created":"2026-09-11","cited_by_count":0,"area":"synth"}],"next_cursor":null}