Zero-Shot and Few-Shot Generalization in Large-Scale Foundation Models using Contrastive Learning

Abstract

The rapid evolution of large-scale foundation models has significantly reshaped the landscape of artificial intelligence, enabling remarkable performance across diverse tasks even with minimal supervision. A crucial capability that has emerged from these models is the ability to generalize in zero-shot and few-shot scenarios, where traditional machine learning methods typically fail due to insufficient examples. This research paper investigates a comprehensive framework that enhances zero-shot and few-shot generalization in foundation models through advanced contrastive learning techniques. Contrastive learning, which aims to maximize agreement between semantically similar representations while distinguishing dissimilar pairs, has shown promise in representation learning for vision, language, and multimodal tasks. However, its role in enabling broader generalization capabilities in foundation models remains an active area of exploration.

 In this work, we propose a unified contrastive learning pipeline that integrates multimodal feature alignment, cross-domain embedding consistency, and adaptive prototype refinement to boost generalization performance. The proposed method builds on large-scale unsupervised pretraining, where the model is exposed to vast, heterogeneous datasets, allowing it to learn universal representations that transfer effectively to novel tasks. By introducing hierarchical contrastive objectives at the token, sentence, and task levels, the model is encouraged to develop representations that not only capture fine-grained semantics but also abstract structural patterns relevant to downstream applications. Additionally, we incorporate a dynamic margin scaling strategy in the contrastive loss to mitigate the representation collapse issue and to maintain near-optimal inter-class separation for low-resource tasks.

 Experimental evaluation is conducted across benchmark datasets in natural language understanding, image classification, visual-question answering, and cross-modal retrieval. The proposed framework demonstrates superior performance compared to existing zero-shot and few-shot learning baselines, including prompt-tuned large language models and contrastive vision-language architectures. Our results confirm that contrastive learning significantly enhances the robustness of foundation models by improving representation diversity and reducing sensitivity to data sparsity. Furthermore, an ablation study reveals the individual contribution of each contrastive component, highlighting the importance of hierarchical alignment in achieving state-of-the-art generalization.