When embeddings have too many dimensions, they may become less interpretable and harder to work with. As the number of dimensions increases, the distance between points in the embedding space also increases, which can lead to sparsity—meaning that most of the embedding space becomes empty or filled with meaningless information. This phenomenon, known as the "curse of dimensionality," can make it more difficult for models to find meaningful patterns and relationships in the data.
High-dimensional embeddings can also lead to increased computational complexity. As the dimensionality grows, it requires more memory to store the embeddings, and the time needed for similarity calculations (such as nearest neighbor searches) also increases. This can be a problem in real-time applications or when dealing with very large datasets.
To mitigate these issues, techniques like dimensionality reduction (e.g., PCA or t-SNE) are often applied to embeddings. These methods reduce the number of dimensions while retaining the most important information, improving both computational efficiency and interpretability. While high-dimensional embeddings can be useful in some cases, finding the right balance of dimensions is key to ensuring that embeddings remain effective and practical.