Dohmatob et al. (2024) provide a theoretical framework explaining that the inclusion of synthetic, AI-generated data in the training corpus can alter or break traditional scaling laws, potentially leading to performance degradation and model collapse.

Authors

• Person: Not available Organization: arXiv
  A Survey on the Theory and Mechanism of Large Language Models

Sources

• A Survey on the Theory and Mechanism of Large Language Models arxiv.org arXiv via serper

Referenced by nodes (1)

• Synthetic data concept