Matryoshka embeddings are a type of hierarchical representation in NLP where embeddings are structured to reflect nested or layered relationships between concepts. The name is inspired by Matryoshka dolls, where smaller dolls fit inside larger ones, symbolizing hierarchical containment.
These embeddings capture the idea that words or phrases can have meanings at different levels of granularity. For instance, in "apple," the word could represent a fruit (general category) or the Apple Inc. company (specific instance). Matryoshka embeddings encode such relationships, enabling models to disambiguate meanings based on context.
They are particularly useful in knowledge graphs, hierarchical classification, and domain-specific tasks where layered relationships between terms must be captured. By organizing embeddings in a nested manner, NLP systems gain better contextual understanding and improved performance in complex reasoning tasks.