Selective Weak-to-Strong Generalization

Understand

Future superhuman models will surpass the ability of humans and humans will only be able to \textit{weakly} supervise superhuman models.

• To alleviate the issue of lacking high-quality data for model alignment, some works on weak-to-strong generalization (W2SG) finetune a strong pretrained model with a weak supervisor so that it can generalize beyond weak supervision.
• However, the invariable use of weak supervision in existing methods exposes issues in robustness, with a proportion of weak labels proving harmful to models.
• In this paper, we propose a selective W2SG framework to avoid using weak supervision when unnecessary.