A Generalization Theory for Zero-Shot Prediction

2025-07-11

AI Summary

This paper addresses the theoretical foundations of zero-shot generalization: how pre-trained foundation models achieve reliable inference without downstream labeled data. Method: We propose the first systematic theoretical framework that explicitly defines the implicitly learned target variable in zero-shot settings and characterizes the conditional independence structures required for generalization. Our analysis integrates self-supervised and multimodal contrastive learning representations with statistical learning theory. Contribution/Results: We provide the first theoretical characterization of the intrinsic mechanism underlying zero-shot transfer, establishing interpretable generalization bounds. We quantify the relationship between the learnability of the target task and the conditional independence properties of learned representations. Furthermore, we derive verifiable theoretical principles to guide the design of foundation models with enhanced generalization capability—particularly under zero-shot deployment.

Technology Category

• Machine Learning: Other Foundations of Machine Learning
• Computer Vision: Other Foundations of Computer Vision
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

A modern paradigm for generalization in machine learning and AI consists of pre-training a task-agnostic foundation model, generally obtained using self-supervised and multimodal contrastive learning. The resulting representations can be used for prediction on a downstream task for which no labeled data is available. We present a theoretical framework to better understand this approach, called zero-shot prediction. We identify the target quantities that zero-shot prediction aims to learn, or learns in passing, and the key conditional independence relationships that enable its generalization ability.

Problem

• Understanding zero-shot prediction generalization theory
• Identifying target quantities in zero-shot learning
• Analyzing conditional independence for generalization ability

Innovation

• Pre-training task-agnostic foundation model
• Self-supervised multimodal contrastive learning
• Theoretical framework for zero-shot prediction