[1] It borrows techniques both from lexical bag-of-words and vector embedding algorithms, and is claimed to perform better than either alone.
[3] There are also extensions of sparse retrieval approaches to the vision-language domain, where these methods are applied to multimodal data, such as combining text with images.
[10] This expansion enables the retrieval of relevant content across different modalities, such as finding images based on text queries or vice versa.
Some implementations of SPLADE have similar latency to Okapi BM25 lexical search while giving as good results as state-of-the-art neural rankers on in-domain data.
[11] The Official SPLADE model weights and training code is released under a Creative Commons NonCommercial license.