@inproceedings{li-etal-2025-large-foundation,
    title = "On Large Foundation Models and {A}lzheimer{'}s Disease Detection",
    author = "Li, Chuyuan  and
      Carenini, Giuseppe  and
      Field, Thalia",
    editor = "Ananiadou, Sophia  and
      Demner-Fushman, Dina  and
      Gupta, Deepak  and
      Thompson, Paul",
    booktitle = "Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health)",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/moar-dois/2025.cl4health-1.13/",
    doi = "10.18653/v1/2025.cl4health-1.13",
    pages = "158--168",
    ISBN = "979-8-89176-238-1",
    abstract = "Large Foundation Models have displayed incredible capabilities in a wide range of domains and tasks. However, it is unclear whether these models match specialist capabilities without special training or fine-tuning. In this paper, we investigate the innate ability of foundation models as neurodegenerative disease specialists. Precisely, we use a language model, Llama-3.1, and a visual language model, Llama3-LLaVA-NeXT, to detect language specificity between Alzheimer{'}s Disease patients and healthy controls through a well-known Picture Description task. Results show that Llama is comparable to supervised classifiers, while LLaVA, despite its additional ``vision'', lags behind."
}