To what extent does token pruning in SPLADE models degrade retrieval accuracy vs. improve latency on multi-hop

Abstract

Abstract: Latency and efficiency issues are often overlooked when evaluating IR models based on Pretrained Language Models (PLMs) in reason of multiple hardware and software testing scenarios. Nevertheless, efficiency is an important part of such systems and should not be overlooked. In this paper, we focus on improving the efficiency of the SPLADE model since it has achieved state-of-the-art zero-shot performance and competitive results on TREC collections. SPLADE efficiency can be controlled via a regularization factor, but solely controlling this regularization has been shown to not be efficient en

Research Question

To what extent does token pruning in SPLADE models degrade retrieval accuracy vs. improve latency on multi-hop QA datasets like HotpotQA and MuSiQue?

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