@inproceedings{zanzotto-etal-2025-position,
    title = "Position Paper: {M}e{M}o: Towards Language Models with Associative Memory Mechanisms",
    author = "Zanzotto, Fabio Massimo  and
      Ruzzetti, Elena Sofia  and
      Xompero, Giancarlo A.  and
      Ranaldi, Leonardo  and
      Venditti, Davide  and
      Ranaldi, Federico  and
      Giannone, Cristina  and
      Favalli, Andrea  and
      Romagnoli, Raniero",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.findings-acl.785/",
    pages = "15169--15180",
    ISBN = "979-8-89176-256-5",
    abstract = "Memorization is a fundamental ability of Transformer-based Large Language Models, achieved through learning. In this position/theory paper, we propose a paradigm shift by designing an architecture to memorize text directly, bearing in mind the principle that memorization precedes learning. We introduce MeMo, a novel architecture for language modeling that explicitly memorizes sequences of tokens in layered associative memories. By design, MeMo offers transparency and the possibility of model editing, including forgetting texts. We experimented with the MeMo architecture, showing the memorization power of the one-layer and the multi-layer configurations."
}