How do I know if I need to normalize the sentence embeddings (for example, applying L2 normalization), and what happens if I don't do it when computing similarities?

When to Normalize Sentence Embeddings You should normalize sentence embeddings (e.g., using L2 normalization) when the similarity metric you’re using depends on the direction of vectors rather than their magnitudes. For example, cosine similarity—a common metric for comparing embeddings—calculates the angle between vectors, which is equivalent to the dot product of L2-normalized vectors. If your embeddings have varying magnitudes (e.g., due to model architecture or training data), normalization ensures that similarity scores aren’t skewed by vector length. For instance, models like BERT or RoBERTa don’t inherently produce normalized embeddings, so applying L2 normalization before computing similarities is often necessary. Conversely, if your model explicitly outputs normalized embeddings (e.g., some sentence-transformers), additional normalization might be redundant.

What Happens If You Skip Normalization? If you don’t normalize embeddings, similarity scores may reflect both the direction and magnitude of vectors. This can lead to misleading results. For example, a long vector (large magnitude) pointing in a slightly similar direction to another vector might produce a higher dot product than a shorter vector pointing in a nearly identical direction. This distorts the semantic similarity you’re trying to measure. Consider two embeddings: one for “a warm, sunny day” (magnitude 5) and another for “hot weather” (magnitude 3). Without normalization, their dot product might prioritize magnitude over semantic alignment, causing “hot weather” to seem less similar to “a warm, sunny day” than it should. Normalization eliminates this bias, ensuring comparisons focus purely on directional alignment.

Practical Implications and Trade-offs Normalization is computationally cheap and easy to implement (e.g., using sklearn.preprocessing.normalize or manual L2 scaling). Skipping it might save minimal computation time but risks inaccurate similarity rankings. For example, in a search system, unnormalized embeddings could prioritize documents with longer text (which often have larger-magnitude embeddings) over shorter, more relevant ones. However, normalization isn’t always required. If your task inherently relies on magnitude (e.g., detecting confidence scores embedded in vector length), preserving raw embeddings makes sense. Always validate by testing both approaches: compute similarities with and without normalization and check which aligns better with your task’s ground truth or expected outcomes.