What is the primary purpose of embeddings in modern NLP?

**Embeddings** are dense numeric vectors that represent text in a way that captures meaning. The defining property: two pieces of text with similar meaning land at nearby points in the vector space, and unrelated pieces land far apart. This matters because once meaning becomes a position in space, comparing meanings becomes geometry. To find documents related to a query, you embed the query, embed every document, and look for the documents whose vectors are closest to the query vector. To cluster sentences by topic, you run a clustering algorithm on the vectors. To classify a new piece of text, you compare its vector to vectors of labelled examples. The other three options in the question describe different operations that have nothing to do with this. Compression makes text smaller, encryption hides it from unauthorised readers, translation rewrites it in another language. None of those produce a vector space where geometric proximity equals semantic similarity. The vectors themselves are produced by an embedding model trained on huge amounts of text. The model has learned how to arrange new text on the map so that the arrangement respects meaning. That is what makes the geometry useful: when the model places vectors well, simple math on top recovers retrieval, clustering, and classification results that match human intuition.

**Embeddings** are dense, fixed-dimensional vectors that map discrete text into a continuous space where semantic similarity becomes geometric proximity. A modern text embedding has a few hundred to a few thousand dimensions. The embedding model is trained so that semantically related texts end up close in that space, while unrelated texts end up far apart. Once that property holds, retrieval, clustering, classification, and recommendation all reduce to elementary operations: nearest-neighbour search, distance-based clustering, vector arithmetic. Option B is correct. Option A confuses embeddings with compression, which preserves the original text byte for byte. Option C confuses them with encryption, which has the strong property that only key-holders can recover the input. Option D confuses them with machine translation, which produces target-language text rather than a vector. The practical use case that has made embeddings ubiquitous in modern NLP is retrieval-augmented generation, where embeddings drive the retrieval step that fetches relevant context for an LLM to read. Without embeddings, retrieval over large corpora would have to rely on lexical keyword matching, which misses semantic matches and forces the system to depend on exact wording.