Papers with State-space models
LOCOST: State-Space Models for Long Document Abstractive Summarization (2024.eacl-long)
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Florian Le Bronnec, Song Duong, Mathieu Ravaut, Alexandre Allauzen, Nancy Chen, Vincent Guigue, Alberto Lumbreras, Laure Soulier, Patrick Gallinari
| Challenge: | State-space models are a low-complexity alternative to transformers for text generation . however, the quadratic complexity of the input length restricts the application of large pretrained models to long texts. |
| Approach: | They propose an encoder-decoder architecture based on state-space models for conditional text generation with long context inputs. |
| Outcome: | The proposed model saves memory and memory during training and inference time while saving 50% and 87% of memory. |
Efficient Unstructured Pruning of Mamba State-Space Models for Resource-Constrained Environments (2025.emnlp-main)
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| Challenge: | State-space models struggle with quadratic computational complexity, limiting their use in long-context tasks and resource-constrained input data. |
| Approach: | They propose a pruning framework specifically tailored for Mamba that reduces parameter counts by 70% with only a 3–9% drop in performance. |
| Outcome: | The proposed pruning framework achieves up to 70% parameter reduction with only a 3–9% drop in performance. |