Embeddings play a critical role in enabling models to handle few-shot and zero-shot learning scenarios by mapping data into structured, semantically meaningful representations. In both cases, embeddings act as a bridge between raw inputs (like text or images) and the model’s ability to generalize from limited or no labeled examples. By converting data into dense numerical vectors, embeddings capture underlying patterns and relationships, allowing models to compare and reason about new inputs even when training data is scarce.
In few-shot learning, embeddings allow models to leverage similarities between a small set of labeled examples and new, unseen data. For example, a language model fine-tuned with embeddings can classify rare text categories using just a few samples by measuring how closely the embeddings of new text match those of the labeled examples. In computer vision, models like Siamese Networks use embeddings to compare image features—such as distinguishing between dog breeds with only five training images per class. The key is that the embedding space clusters semantically similar items, so even minimal examples provide a meaningful reference for the model to generalize.
For zero-shot learning, embeddings enable models to handle tasks with no labeled data by aligning inputs with external knowledge or metadata. For instance, CLIP (Contrastive Language-Image Pretraining) maps images and text descriptions into a shared embedding space. This allows it to classify images into unseen categories (e.g., “zebra”) by comparing image embeddings to text embeddings of category names, even if “zebra” was never part of its training labels. Similarly, in NLP, models like BERT can associate embeddings of prompts (e.g., “Translate English to French: 'cat’”) with expected outputs by understanding the semantic intent encoded in the embeddings. By structuring embeddings to reflect relationships across modalities or tasks, models can perform entirely new operations without direct training examples.