What is the throughput (queries per second) trade-off for dense retrievers (e.g., Contriever) on MuSiQue 2-hop

Abstract

Abstract: We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5\% and 17.0\%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturat

Research Question

What is the throughput (queries per second) trade-off for dense retrievers (e.g., Contriever) on MuSiQue 2-hop vs 3-hop queries under fixed GPU memory, compared to sparse retrievers on CPU?

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