Theoretical Analysis of Weak-to-Strong Generalization

TL;DR

This paper provides a theoretical framework explaining how strong models can improve by learning from weaker teachers, especially in scenarios with incomplete or incorrect labels, through concepts of pseudolabel correction and coverage expansion.

Contribution

It introduces a new theoretical bound based on data distribution and hypothesis class expansion properties, addressing limitations of existing weak supervision theories.

Findings

Theoretical bounds account for pseudolabel correction and coverage expansion effects.

Empirical evidence supports the practical relevance of the expansion properties.

Weak-to-strong generalization occurs when strong models correct weak teacher mistakes without additional error.

Abstract

Strong student models can learn from weaker teachers: when trained on the predictions of a weaker model, a strong pretrained student can learn to correct the weak model's errors and generalize to examples where the teacher is not confident, even when these examples are excluded from training. This enables learning from cheap, incomplete, and possibly incorrect label information, such as coarse logical rules or the generations of a language model. We show that existing weak supervision theory fails to account for both of these effects, which we call pseudolabel correction and coverage expansion, respectively. We give a new bound based on expansion properties of the data distribution and student hypothesis class that directly accounts for pseudolabel correction and coverage expansion. Our bounds capture the intuition that weak-to-strong generalization occurs when the strong model is unable to fit the mistakes of the weak teacher without incurring additional error. We show that these expansion properties can be checked from finite data and give empirical evidence that they hold in practice.