Learned sparse retrieval

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"SPLADE" redirects here. For the eating utensil, see splayd.

Learned sparse retrieval or sparse neural search is an approach to

vector embedding algorithms, and is claimed to perform better than either alone. The best-known sparse neural search systems are SPLADE[2] and its successor SPLADE v2.[3] Others include DeepCT,[4] uniCOIL,[5] EPIC,[6] DeepImpact,[7] TILDE and TILDEv2,[8] Sparta,[9] SPLADE-max, and DistilSPLADE-max.[3]

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.[10]

The Official SPLADE model weights and training code is released under a Creative Commons NonCommercial license.[11] But there are other independent implementations of SPLADE++ (a variant of SPLADE models) that are released under permissive licenses.

SPRINT is a toolkit for evaluating neural sparse retrieval systems.[12]

External links

Notes

  1. S2CID 257585074
    .
  2. S2CID 235792467
    .
  3. ^
    arXiv:2109.10086v1 [cs.IR
    ].
  4. S2CID 218521094
    .
  5. arXiv:2106.14807 [cs.IR
    ].
  6. S2CID 216641912
    .
  7. S2CID 233394068
    .
  8. arXiv:2108.08513 [cs.IR
    ].
  9. arXiv:2009.13013 [cs.CL
    ].
  10. S2CID 250340284
    .
  11. ^ "splade/LICENSE at main · naver/splade". GitHub. Retrieved 2023-08-25.
  12. S2CID 259949923
    .
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