How does the inference throughput (queries per second) of SPLADE-v3 compare to ColBERT-v2 under controlled spa

Abstract

Abstract: Late interaction neural IR models like ColBERT offer a competitive effectiveness-efficiency trade-off across many benchmarks. However, they require a huge memory space to store the contextual representation for all the document tokens. Some works have proposed using either heuristics or statistical-based techniques to prune tokens from each document. This however doesn't guarantee that the removed tokens have no impact on the retrieval score. Our work uses a principled approach to define how to prune tokens without impacting the score between a document and a query. We introduce three regulari

Research Question

How does the inference throughput (queries per second) of SPLADE-v3 compare to ColBERT-v2 under controlled sparsity regularization on the BEIR benchmark?

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