preembedding

Preembedding, or precomputed embeddings, refers to the practice of computing vector representations of data items before using them in downstream tasks. The goal is to enable models to operate on numeric features directly rather than raw data, often improving speed and consistency at inference time.

Embeddings are dense, real-valued vectors that place similar items near each other in a high-dimensional space. Preembedding can be performed with a variety of methods, including word-level embeddings (Word2Vec, GloVe, fastText), sentence or document embeddings from transformer-based models, and graph or multimodal embeddings. The resulting vectors can be static or updated periodically.

Once created, embeddings are typically stored in lookup tables or databases and used by downstream components. For large collections, efficient similarity search is often needed, using approximate nearest neighbor libraries and indexing structures to enable fast retrieval and ranking.

Advantages of preembedding include reduced computational load during inference and the ability to leverage large unlabeled corpora or pooled representations. Limitations include potential domain drift, increased memory requirements, dependency on the quality of the embedding model, and the risk that static embeddings do not capture task-specific nuances without fine-tuning.

In practice, preembedding is commonly used in natural language processing, information retrieval, and recommender systems as a preprocessing step or as a feature extraction strategy within larger pipelines. It complements end-to-end learning by separating representation learning from task-specific learning.