Dense Sparse Retrieval: Using Sparse Language Models for Inference Efficient Dense Retrieval

• URL: http://arxiv.org/abs/2304.00114v1
• Date: Fri, 31 Mar 2023 20:21:32 GMT
• Title: Dense Sparse Retrieval: Using Sparse Language Models for Inference Efficient Dense Retrieval
• Authors: Daniel Campos, ChengXiang Zhai
• Abstract summary: We study how sparse language models can be used for dense retrieval to improve inference efficiency. We find that sparse language models can be used as direct replacements with little to no drop in accuracy and up to 4.3x improved inference speeds.
• Score: 37.22592489907125
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vector-based retrieval systems have become a common staple for academic and industrial search applications because they provide a simple and scalable way of extending the search to leverage contextual representations for documents and queries. As these vector-based systems rely on contextual language models, their usage commonly requires GPUs, which can be expensive and difficult to manage. Given recent advances in introducing sparsity into language models for improved inference efficiency, in this paper, we study how sparse language models can be used for dense retrieval to improve inference efficiency. Using the popular retrieval library Tevatron and the MSMARCO, NQ, and TriviaQA datasets, we find that sparse language models can be used as direct replacements with little to no drop in accuracy and up to 4.3x improved inference speeds

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