Getting a GRIP on Graph-Based Search for Learned Sparse Retrieval

Learned sparse retrieval (LSR) has recently gained traction in the information retrieval community for the ability to combine the generalization capabilities of neural representations with the interpretability of keyword-based search. A number of recent works have demonstrated that inverted indexes, when appropriately modified for learned sparse embeddings, achieve state-of-the-art recall-latency tradeoffs for LSR, often outperforming graph-based indexes by substantial margins in latency at a fixed recall target. As a result, the community has generally concluded that graph-based approximate nearest neighbor (ANN) search is inferior to inverted indexes for LSR. This paper challenges this conventional wisdom. Our key insight is that, while applying standard graph-based ANN indexes out-of-the-box to sparse vectors leads to uncompetitive performance, we can overcome these shortcomings by infusing the strengths of inverted indexes into graph-based search. In particular, we introduce three techniques to accelerate graph-based LSR: (i) term-based entry point seeding, (ii) approximate routing via sparse sketching, and (iii) sparse partition-based graph construction. We combine these techniques into a new graph-based ANN system for LSR we call GRIP. On the MS MARCO v2 benchmark (138M passages) with SPLADE embeddings, GRIP achieves a $2.5\times$ latency improvement over Seismic, a state-of-the-art inverted index tailored for LSR, at 95% recall@10. Ultimately, our results call into question the default choice of inverted indexes for deploying learned sparse retrieval.