{"total":23,"rows":[{"id":"2404.07503","title":"Best Practices and Lessons Learned on Synthetic Data","created":"2024-04-11","cited_by_count":106,"area":"synth"},{"id":"2509.07414","title":"Language Self-Play For Data-Free Training","created":"2025-09-09","cited_by_count":43,"area":"synth"},{"id":"2410.12896","title":"A Survey on Data Synthesis and Augmentation for Large Language Models","created":"2024-10-16","cited_by_count":41,"area":"synth"},{"id":"2310.00158","title":"Feedback-guided Data Synthesis for Imbalanced Classification","created":"2023-09-29","cited_by_count":33,"area":"synth"},{"id":"2509.15155","title":"Self-Improving Embodied Foundation Models","created":"2025-09-18","cited_by_count":32,"area":"synth"},{"id":"2510.12784","title":"SRUM: Fine-Grained Self-Rewarding for Unified Multimodal Models","created":"2025-10-14","cited_by_count":22,"area":"synth"},{"id":"2506.07570","title":"OptiScene: LLM-driven Indoor Scene Layout Generation via Scaled Human-aligned Data Synthesis and Multi-Stage Preference Optimization","created":"2025-06-09","cited_by_count":14,"area":"synth"},{"id":"2403.19880","title":"Vision-Language Synthetic Data Enhances Echocardiography Downstream Tasks","created":"2024-03-28","cited_by_count":6,"area":"synth"},{"id":"2410.09865","title":"SynFER: Towards Boosting Facial Expression Recognition with Synthetic Data","created":"2024-10-13","cited_by_count":6,"area":"synth"},{"id":"2510.26046","title":"Bias-Corrected Data Synthesis for Imbalanced Learning","created":"2025-10-30","cited_by_count":6,"area":"synth"},{"id":"2506.06380","title":"Beyond the Norm: A Survey of Synthetic Data Generation for Rare Events","created":"2025-06-04","cited_by_count":5,"area":"synth"},{"id":"2406.18790","title":"MUMU: Bootstrapping Multimodal Image Generation from Text-to-Image Data","created":"2024-06-26","cited_by_count":4,"area":"synth"},{"id":"2603.09625","title":"Grounding Synthetic Data Generation With Vision and Language Models","created":"2026-03-10","cited_by_count":3,"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":"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":"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":"2606.13732","title":"When Sample Selection Bias Precipitates Model Collapse","created":"2026-06-11","cited_by_count":1,"area":"synth"},{"id":"2504.04510","title":"Attributed Synthetic Data Generation for Zero-shot Domain-specific Image Classification","created":"2025-04-06","cited_by_count":0,"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":"2605.20279","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":"2608.13932","title":"Post-training Quantization for Hybrid Iterative Generative Models","created":"2026-08-14","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":"2608.29674","title":"Creation begins with understanding: LLMs as strategy designers for privacy-preserving tabular data synthesis","created":"2026-08-30","cited_by_count":0,"area":"synth"}],"next_cursor":null}