Model Collapse Demystified: The Case of Regression

12 February 2024

Papers citing "Model Collapse Demystified: The Case of Regression"

9 / 9 papers shown

Title
Recursive Training Loops in LLMs: How training data properties modulate distribution shift in generated data? Grgur Kovač Jérémy Perez Rémy Portelas Peter Ford Dominey Pierre-Yves Oudeyer 33 0 0 04 Apr 2025
Position: Model Collapse Does Not Mean What You Think Rylan Schaeffer Joshua Kazdan Alvan Caleb Arulandu Sanmi Koyejo 60 0 0 05 Mar 2025
Spend Wisely: Maximizing Post-Training Gains in Iterative Synthetic Data Boostrapping Pu Yang Yunzhen Feng Ziyuan Chen Yuhang Wu Zhuoyuan Li DiffM 101 0 0 31 Jan 2025
Collapse or Thrive? Perils and Promises of Synthetic Data in a Self-Generating World Joshua Kazdan Rylan Schaeffer Apratim Dey Matthias Gerstgrasser Rafael Rafailov D. Donoho Sanmi Koyejo 47 11 0 22 Oct 2024
How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse M. Seddik Suei-Wen Chen Soufiane Hayou Pierre Youssef Merouane Debbah 42 30 0 07 Apr 2024
Data Feedback Loops: Model-driven Amplification of Dataset Biases Rohan Taori Tatsunori B. Hashimoto 64 43 0 08 Sep 2022
Zero-Shot Text-to-Image Generation Aditya A. Ramesh Mikhail Pavlov Gabriel Goh Scott Gray Chelsea Voss Alec Radford Mark Chen Ilya Sutskever VLM 255 4,777 0 24 Feb 2021
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks Blake Bordelon Abdulkadir Canatar C. Pehlevan 133 201 0 07 Feb 2020
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 228 4,460 0 23 Jan 2020