Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Predicting Efficiency/Effectiveness Trade-offs for Dense vs. Sparse
  Retrieval Strategy Selection   Over the last few years, contextualized pre-trained transformer models such
as BERT have provided substantial improvements on information retrieval tasks.
Recent approaches based on pre-trained transformer models such as BERT,
fine-tune dense low-dimensional contextualized representations of queries and
documents in embedding space. While these dense retrievers enjoy substantial
retrieval effectiveness improvements compared to sparse retrievers, they are
computationally intensive, requiring substantial GPU resources, and dense
retrievers are known to be more expensive from both time and resource
perspectives. In addition, sparse retrievers have been shown to retrieve
complementary information with respect to dense retrievers, leading to
proposals for hybrid retrievers. These hybrid retrievers leverage low-cost,
exact-matching based sparse retrievers along with dense retrievers to bridge
the semantic gaps between query and documents. In this work, we address this
trade-off between the cost and utility of sparse vs dense retrievers by
proposing a classifier to select a suitable retrieval strategy (i.e., sparse
vs. dense vs. hybrid) for individual queries. Leveraging sparse retrievers for
queries which can be answered with sparse retrievers decreases the number of
calls to GPUs. Consequently, while utility is maintained, query latency
decreases. Although we use less computational resources and spend less time, we
still achieve improved performance. Our classifier can select between sparse
and dense retrieval strategies based on the query alone. We conduct experiments
on the MS MARCO passage dataset demonstrating an improved range of
efficiency/effectiveness trade-offs between purely sparse, purely dense or
hybrid retrieval strategies, allowing an appropriate strategy to be selected
based on a target latency and resource budget.

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The categories of this paper are: gr-qc
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The categories of this paper are: hep-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-lat
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The categories of this paper are: q-fin
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The categories of this paper are: comp-gas
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The categories of this paper are: nucl-th
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The categories of this paper are: funct-an
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The categories of this paper are: stat
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The categories of this paper are: alg-geom
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The categories of this paper are: nlin
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The categories of this paper are: quant-ph
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The categories of this paper are: econ
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The categories of this paper are: math
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The categories of this paper are: adap-org
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The categories of this paper are: mtrl-th