Predicting Efficiency/Effectiveness Trade-offs for Dense vs. Sparse Retrieval Strategy Selection

Over the last few years, contextualized pre-trained transformer models such\nas BERT have provided substantial improvements on information retrieval tasks.\nRecent approaches based on pre-trained transformer models such as BERT,\nfine-tune dense low-dimensional contextualized representations of queries and\ndocuments in embedding space. While these dense retrievers enjoy substantial\nretrieval effectiveness improvements compared to sparse retrievers, they are\ncomputationally intensive, requiring substantial GPU resources, and dense\nretrievers are known to be more expensive from both time and resource\nperspectives. In addition, sparse retrievers have been shown to retrieve\ncomplementary information with respect to dense retrievers, leading to\nproposals for hybrid retrievers. These hybrid retrievers leverage low-cost,\nexact-matching based sparse retrievers along with dense retrievers to bridge\nthe semantic gaps between query and documents. In this work, we address this\ntrade-off between the cost and utility of sparse vs dense retrievers by\nproposing a classifier to select a suitable retrieval strategy (i.e., sparse\nvs. dense vs. hybrid) for individual queries. Leveraging sparse retrievers for\nqueries which can be answered with sparse retrievers decreases the number of\ncalls to GPUs. Consequently, while utility is maintained, query latency\ndecreases. Although we use less computational resources and spend less time, we\nstill achieve improved performance. Our classifier can select between sparse\nand dense retrieval strategies based on the query alone. We conduct experiments\non the MS MARCO passage dataset demonstrating an improved range of\nefficiency/effectiveness trade-offs between purely sparse, purely dense or\nhybrid retrieval strategies, allowing an appropriate strategy to be selected\nbased on a target latency and resource budget.\n