SPLADE (Sparse Lexical and Expansion model), published by Thibault Formal, Benjamin Piwowarski, and Stephane Clinchant in July 2021, sits between two retrieval traditions. Classic search engines use sparse, term-based representations like BM25 that match exact words through an inverted index; modern neural retrievers use dense embeddings that capture meaning but lose exact-match precision. SPLADE produces representations that are sparse, like the classic approach, but learned by a neural network, so it can also expand a query or document with related terms the original text did not contain.
The model uses explicit sparsity regularization and a log-saturation effect on term weights to keep the output sparse and controllable, and it is trained end-to-end in a single stage. Because the result is sparse and term-based, it can be served with the same efficient inverted-index machinery that powers traditional search, while still benefiting from neural semantic learning.
SPLADE became a popular choice for the first-stage ranking step in retrieval pipelines, where you need to pull a candidate set from millions of documents quickly. For a business, it offers a middle path: the speed and exact-match reliability of keyword search combined with the semantic recall of neural models, often without giving up existing search infrastructure.