When to Normalize Sentence Embeddings You should normalize sentence embeddings (e.g., using L2 normalization) when your task relies on cosine similarity or when vector magnitude is irrelevant to the comparison. For example, in semantic search or clustering, cosine similarity measures the angle between vectors, ignoring their lengths. Normalization ensures all vectors have unit length, making cosine similarity equivalent to a dot product. Without normalization, embeddings with larger magnitudes (e.g., from longer sentences or model-specific biases) will dominate similarity scores, even if their semantic content isn’t closer. For instance, a BERT embedding for a long paragraph might artificially score higher in similarity than a shorter, more relevant sentence simply because its raw vector has a larger magnitude.
What Happens If You Skip Normalization If you compute similarities without normalizing, the dot product (or unnormalized cosine similarity) will conflate semantic similarity with vector magnitude. This can lead to misleading results. For example, in a recommendation system, a movie plot summary with many keywords might have a high-magnitude embedding, causing it to match poorly with user preferences that are semantically closer to a lower-magnitude embedding. Similarly, in a classification task, unnormalized embeddings might cause the model to overweight noisy features encoded in vector magnitude rather than semantic relationships. This effect is especially pronounced in models like Word2Vec or GloVe, where word frequency can influence vector magnitude.
Practical Considerations
Normalization is often a default step in pipelines using embeddings for similarity tasks. However, there are exceptions: if your downstream algorithm (e.g., a neural network layer) learns to account for magnitude differences, normalization might not be needed. For example, a classifier trained on raw embeddings could theoretically adapt to magnitude variations. But in practice, normalization simplifies training and improves interpretability. To test whether normalization matters for your use case, compare results with and without it. If performance degrades without normalization (e.g., irrelevant matches in retrieval tasks), stick to normalizing. Tools like sentence-transformers often normalize by default for cosine similarity, reflecting its importance in standard workflows.