State Space Models are Strong Text Rerankers (2412.14354v1)

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.

State Space Models as Text Rerankers: A Comprehensive Evaluation

The paper entitled "State Space Models are Strong Text Rerankers" presents a rigorous exploration of state space models, specifically the Mamba architectures, in the context of text reranking tasks. The authors investigate these models as promising alternatives to the transformer architectures that currently dominate the fields of NLP and information retrieval (IR).

Context and Motivation

Transformers, despite their popularity and effectiveness in capturing long-range dependencies through self-attention mechanisms, face limitations in inference efficiency, particularly with longer contexts. This inefficiency has catalyzed interest in alternatives such as state space models (SSMs), which offer a compelling advantage with $O(1)$ time complexity for inference, theoretically suggesting more scalable computation.

SSMs, including the novel models Mamba-1 and Mamba-2, align with approaches found in convolutional and recurrent neural networks and related signal processing literature. Yet, their applicability and efficacy in tasks such as text reranking remain underexamined. Text reranking necessitates nuanced query-document interactions and an ability to comprehend extensive contexts, making it an ideal testbed for SSM-based architectures.

Research Focus

The paper benchmarks Mamba models against transformer-based architectures in terms of performance metrics and computational efficiency on text reranking tasks. Through this investigation, the authors pursue two primary research questions:

1. Performance RQ: How does the reranking performance of Mamba models compare with transformer-based models?
2. Efficiency RQ: What is the relative efficiency of Mamba models concerning training throughputs and inference speeds?

Experimental Setup

The experimental framework involves comprehensive comparisons across a range of state space and transformer models, covering various scales and pre-training methodologies. Leveraging established retrieval-reranking pipelines, the paper engages with both passage and document reranking tasks, examining models on datasets like MS MARCO and BEIR, and using metrics such as MRR and NDCG to quantify performance.

The Mamba models were assessed along the lines of architecture (Mamba-1 vs. Mamba-2), model size, and pre-training volumes, juxtaposed with transformer models like BERT, RoBERTa, and newer entrants like Llama-3.2-1B, along with encoder-only, encoder-decoder, and decoder-only paradigms.

Key Findings

1. Performance: The Mamba models demonstrate strong reranking capabilities, rivaling similarly sized transformers. Particularly, Mamba-2 shows enhanced performance over Mamba-1, suggesting that the refined architecture of Mamba-2 provides better response characteristics in reranking contexts.
2. Efficiency: Despite their theoretical complexity advantages, Mamba models exhibited lower training and inference speeds compared to optimized transformers utilizing flash attention, such as the LlaMA-3.2-1B model. This indicates room for improvement, especially in harnessing the potential efficiency benefits of state space models.
3. Scalability and Contextual Depth: For large-scale models and tasks involving longer context windows, Mamba models were adept, though current state-of-the-art transformers pre-trained extensively on massive datasets still held the upper hand in performance.

Implications and Future Directions

These insights affirm the Mamba models as viable and competitive transformer alternatives for IR tasks. The paper underscores the continued potential of state space models in offering computationally efficient solutions while achieving comparable performance metrics in NLP tasks.

Future research might explore hybrid architectures combining the strengths of both transformers and SSMs. Additionally, investigating parameter efficiency techniques and refining hardware specific optimizations could further bolster Mamba’s application in large-scale IR environments.

Through its methodical approach and substantial empirical analysis, this paper adds a critical perspective to the dialogue on optimizing text reranking architectures, encouraging exploration into nuanced model designs beyond the conventional transformer-based frameworks.