# How Effective are State Space Models for Machine Translation?

arXiv: https://arxiv.org/abs/2407.05489 · HTML: https://mlsanity.com/paper/2407.05489/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Core SSM architectures for language, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 29 of its 46 references that are in the map.
- First posted on arXiv 7 Jul 2024.

## Placed in

- State-space sequence models (S4, Mamba) / [Core SSM architectures for language](https://mlsanity.com/ssm/core-ssm-architectures-for-language/)

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention](https://mlsanity.com/paper/2006.16236/) (2020)
- [GLU Variants Improve Transformer](https://mlsanity.com/paper/2002.05202/) (2020)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling](https://mlsanity.com/paper/2304.01373/) (2023)
- [Generating Long Sequences with Sparse Transformers](https://mlsanity.com/paper/1904.10509/) (2019)
- [RWKV: Reinventing RNNs for the Transformer Era](https://mlsanity.com/paper/2305.13048/) (2023)
- [Simplified State Space Layers for Sequence Modeling](https://mlsanity.com/paper/2208.04933/) (2022)
- [HiPPO: Recurrent Memory with Optimal Polynomial Projections](https://mlsanity.com/paper/2008.07669/) (2020)
- [Gated Linear Attention Transformers with Hardware-Efficient Training](https://mlsanity.com/paper/2312.06635/) (2023)
- [Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models](https://mlsanity.com/paper/2402.19427/) (2024)
- [Repeat After Me: Transformers are Better than State Space Models at Copying](https://mlsanity.com/paper/2402.01032/) (2024)
- [Transformer Dissection: A Unified Understanding of Transformer's Attention via the Lens of Kernel](https://mlsanity.com/paper/1908.11775/) (2019)
- [Simple linear attention language models balance the recall-throughput tradeoff](https://mlsanity.com/paper/2402.18668/) (2024)
- [Zoology: Measuring and Improving Recall in Efficient Language Models](https://mlsanity.com/paper/2312.04927/) (2023)
- [Zamba: A Compact 7B SSM Hybrid Model](https://mlsanity.com/paper/2405.16712/) (2024)
- [Document-Level Machine Translation with Large Language Models](https://mlsanity.com/paper/2304.02210/) (2023)
- [In-Context Language Learning: Architectures and Algorithms](https://mlsanity.com/paper/2401.12973/) (2024)
- [Detecting and Mitigating Hallucinations in Machine Translation: Model Internal Workings Alone Do Well, Sentence Similarity Even Better](https://mlsanity.com/paper/2212.08597/) (2022)
- [Measuring and Increasing Context Usage in Context-Aware Machine Translation](https://mlsanity.com/paper/2105.03482/) (2021)
- [Sequence Length is a Domain: Length-based Overfitting in Transformer Models](https://mlsanity.com/paper/2109.07276/) (2021)
- [Never Train from Scratch: Fair Comparison of Long-Sequence Models Requires Data-Driven Priors](https://mlsanity.com/paper/2310.02980/) (2023)
- [State Spaces Aren't Enough: Machine Translation Needs Attention](https://mlsanity.com/paper/2304.12776/) (2023)

## Built on by (most cited first, 6 of 6)

- [A Survey of Mamba](https://mlsanity.com/paper/2408.01129/) (2024)
- [LaTIM: Measuring Latent Token-to-Token Interactions in Mamba Models](https://mlsanity.com/paper/2502.15612/) (2025)
- [Systematic Generalization in Language Models Scales with Information Entropy](https://mlsanity.com/paper/2505.13089/) (2025)
- [Efficient Extractive Summarization with MAMBA-Transformer Hybrids for Low-Resource Scenarios](https://mlsanity.com/paper/2603.01288/) (2026)
- [Doc2FRC: Length-Consistent Document-Level Machine Translation via Fixed-Range Chunking](https://mlsanity.com/paper/2609.12674/) (2026)
- [Length Generalization Needs Proper Regularization](https://mlsanity.com/paper/2610.04518/) (2026)

## Abstract (arXiv)

Transformers are the current architecture of choice for NLP, but their attention layers do not scale well to long contexts. Recent works propose to replace attention with linear recurrent layers -- this is the case for state space models, which enjoy efficient training and inference. However, it remains unclear whether these models are competitive with transformers in machine translation (MT). In this paper, we provide a rigorous and comprehensive experimental comparison between transformers and linear recurrent models for MT. Concretely, we experiment with RetNet, Mamba, and hybrid versions of Mamba which incorporate attention mechanisms. Our findings demonstrate that Mamba is highly competitive with transformers on sentence and paragraph-level datasets, where in the latter both models benefit from shifting the training distribution towards longer sequences. Further analysis show that integrating attention into Mamba improves translation quality, robustness to sequence length extrapolation, and the ability to recall named entities.