Papers
Communities
Events
Blog
Pricing
Search
Open menu
Home
Papers
2311.16822
Cited By
Large Language Models Suffer From Their Own Output: An Analysis of the Self-Consuming Training Loop
28 November 2023
Martin Briesch
Dominik Sobania
Franz Rothlauf
Re-assign community
ArXiv
PDF
HTML
Papers citing
"Large Language Models Suffer From Their Own Output: An Analysis of the Self-Consuming Training Loop"
8 / 8 papers shown
Title
Information Retrieval in the Age of Generative AI: The RGB Model
M. Garetto
Alessandro Cornacchia
Franco Galante
Emilio Leonardi
A. Nordio
A. Tarable
98
0
0
29 Apr 2025
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
56
0
0
05 Mar 2025
The Best Instruction-Tuning Data are Those That Fit
Dylan Zhang
Qirun Dai
Hao Peng
ALM
115
3
0
06 Feb 2025
Self-Improving Transformers Overcome Easy-to-Hard and Length Generalization Challenges
Nayoung Lee
Ziyang Cai
Avi Schwarzschild
Kangwook Lee
Dimitris Papailiopoulos
ReLM
VLM
LRM
AI4CE
73
4
0
03 Feb 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
45
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
31
30
0
07 Apr 2024
Training language models to follow instructions with human feedback
Long Ouyang
Jeff Wu
Xu Jiang
Diogo Almeida
Carroll L. Wainwright
...
Amanda Askell
Peter Welinder
Paul Christiano
Jan Leike
Ryan J. Lowe
OSLM
ALM
303
11,881
0
04 Mar 2022
1