State Space Models are Strong Text Rerankers

• URL: http://arxiv.org/abs/2412.14354v3
• Date: Tue, 22 Apr 2025 17:44:39 GMT
• Title: State Space Models are Strong Text Rerankers
• Authors: Zhichao Xu, Jinghua Yan, Ashim Gupta, Vivek Srikumar,
• Abstract summary: State space models (SSMs) like Mamba offer promising advantages.<n>Despite their potential, SSMs' effectiveness at text reranking remains underexplored.<n>Mamba architectures achieve competitive text ranking performance, comparable to transformer-based models of similar size.
• Score: 33.41687512973575
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Transformers dominate NLP and IR; but their inference inefficiencies and challenges in extrapolating to longer contexts have sparked interest in alternative model architectures. Among these, state space models (SSMs) like Mamba offer promising advantages, particularly $O(1)$ time complexity in inference. Despite their potential, SSMs' effectiveness at text reranking -- a task requiring fine-grained query-document interaction and long-context understanding -- remains underexplored. This study benchmarks SSM-based architectures (specifically, Mamba-1 and Mamba-2) against transformer-based models across various scales, architectures, and pre-training objectives, focusing on performance and efficiency in text reranking tasks. We find that (1) Mamba architectures achieve competitive text ranking performance, comparable to transformer-based models of similar size; (2) they are less efficient in training and inference compared to transformers with flash attention; and (3) Mamba-2 outperforms Mamba-1 in both performance and efficiency. These results underscore the potential of state space models as a transformer alternative and highlight areas for improvement in future IR applications.

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