Finite-sample uniform generalization for generative and vision–language models

Determine finite-sample structural conditions under which modern generative models and vision–language models produce predictions that generalize uniformly across inputs, classes, and subpopulations, rather than only on average, so that worst-case errors and miscalibration are controlled across the entire input domain.

Background

The paper focuses on reliability requirements in biomedical applications, where models must be accurate and well calibrated not only on average but uniformly across inputs and subgroups. The authors emphasize that despite strong empirical performance of generative and vision–LLMs with moderate data, there is a key unresolved issue about when such uniform generalization and calibration can be expected in finite-sample regimes.

They propose analyzing induced families of classifiers via prompt embeddings and derive uniform convergence bounds under Lipschitz stability and low effective dimension, but the broader question of identifying general finite-sample conditions for uniform generalization and calibration across diverse settings remains unresolved and motivates their study.