@inproceedings{ghattas-etal-2025-pruning,
    title = "On Pruning State-Space {LLM}s",
    author = "Ghattas, Tamer  and
      Hassid, Michael  and
      Schwartz, Roy",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.950/",
    pages = "18811--18825",
    ISBN = "979-8-89176-332-6",
    abstract = "Recent work proposed state-space models (SSMs) as an efficient alternative to transformer-based LLMs. Can these models be pruned to further reduce their computation costs? We adapt several pruning methods to the SSM structure, and apply them to four SSM-based LLMs across multiple tasks. We find that such models are quite robust to some pruning methods (e.g., WANDA), while using other methods lead to fast performance degradation."
}