Learned Sparse Retrieval

Learned Sparse Retrieval

Learned Sparse Retrieval (LSR) uses neural models to produce sparse document/query representations that can be stored in and searched with traditional Inverted Index infrastructure, combining neural effectiveness with sparse retrieval efficiency.

Approaches

Method	Type	Key Idea
doc2query / docTTTTTquery	Document Expansion	Generate expansion terms with seq2seq model
DeepCT	Term reweighting	Predict context-aware term importance
DeepImpact	Term reweighting	Predict impact scores per term
uniCOIL	Token weights	Single-vector per-token importance
SPLADE	Full vocabulary	Log-saturated weights over entire vocabulary

Why LSR?

Best of Both Worlds

• Like BM25: Uses inverted index → fast retrieval, scalable
• Like dense retrieval: Learns semantic term importance from data
• Plus expansion: Can add semantically related terms not in the original text

Appears In

• IR-L07 - Learned Sparse Retrieval