Gan and Liu (2025) propose a 'reverse-bottleneck' framework, which posits that a post-trained model’s generalization error upper bound is negatively correlated with the 'information gain' obtained from the generative model.

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)

• generative models concept