Production search solutions often need the benefits of exact matching and semantic similarity — who wouldn’t want to have it all?
The most famous to-go approach is hybrid search, which combines old but gold lexical methods with dense retrieval models. Hybrid search is famous for a reason; however, due to its dual component nature, taking the best of both worlds, it also takes the worst — propagates all the intricacies of vector search (heavy vectors, capricious indexes) and limitations of lexical approaches (low recall).
A less famous solution is sparse neural retrieval — models, which make exact matching semantically aware, can distinguish “a fruit bat” and “a baseball bat”. You might know sparse neural retrieval for SPLADE, a leader in sparse neural benchmarks & a heavy model creating not-so-sparse vectors with its query/document extension mechanisms.
Sparse neural retrieval seems pitch-perfect from afar: inverted indices and semantical understanding combined. It’s perhaps overlooked since many attempts to make it lightweight & performant on out-of-domain data failed.
miniCOIL is our shot to give sparse neural retrieval more deserved attention — a lightweight model understanding words’ meaning within the context, performant on out-of-domain datasets and easy to adapt to custom data.
In this talk, after an introduction in the context of sparse neural retrieval, we will show the architecture behind miniCOIL and demonstrate its capabilities.