

Sparse Late Interaction Retrieval model that combines the benefits of sparse representations with late interaction mechanisms. Provides efficient storage and fast retrieval while maintaining the accuracy advantages of token-level matching in sparse embedding space.
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SPLATE (Sparse Late Interaction) is a retrieval model that extends late interaction concepts to sparse representations, combining the storage efficiency of sparse embeddings with the accuracy benefits of token-level matching.
SPLATE bridges two important approaches in neural information retrieval:
score(Q,D) = Σ_q max_d sim(q_i, d_j)
Where q_i and d_j are sparse vectors
SPLATE represents active research in efficient neural retrieval, published in 2024 with ongoing development in the information retrieval community.
SPLATE contributes to the ongoing research in finding optimal trade-offs between storage efficiency, retrieval accuracy, and computational cost in neural information retrieval systems.