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        <title>On Large Foundation Models and Alzheimer’s Disease Detection</title>
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    <name type="personal">
        <namePart type="given">Chuyuan</namePart>
        <namePart type="family">Li</namePart>
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    <name type="personal">
        <namePart type="given">Giuseppe</namePart>
        <namePart type="family">Carenini</namePart>
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    <name type="personal">
        <namePart type="given">Thalia</namePart>
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            <title>Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health)</title>
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        <name type="personal">
            <namePart type="given">Sophia</namePart>
            <namePart type="family">Ananiadou</namePart>
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        <name type="personal">
            <namePart type="given">Dina</namePart>
            <namePart type="family">Demner-Fushman</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Deepak</namePart>
            <namePart type="family">Gupta</namePart>
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            <namePart type="given">Paul</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2025.cl4health-1.13</identifier>
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        <date>2025-05</date>
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%0 Conference Proceedings
%T On Large Foundation Models and Alzheimer’s Disease Detection
%A Li, Chuyuan
%A Carenini, Giuseppe
%A Field, Thalia
%Y Ananiadou, Sophia
%Y Demner-Fushman, Dina
%Y Gupta, Deepak
%Y Thompson, Paul
%S Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health)
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-238-1
%F li-etal-2025-large-foundation
%X 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.
%R 10.18653/v1/2025.cl4health-1.13
%U https://aclanthology.org/2025.cl4health-1.13/
%U https://doi.org/10.18653/v1/2025.cl4health-1.13
%P 158-168

Markdown (Informal)

[On Large Foundation Models and Alzheimer’s Disease Detection](https://aclanthology.org/2025.cl4health-1.13/) (Li et al., CL4Health 2025)

• On Large Foundation Models and Alzheimer’s Disease Detection (Li et al., CL4Health 2025)

ACL

• Chuyuan Li, Giuseppe Carenini, and Thalia Field. 2025. On Large Foundation Models and Alzheimer’s Disease Detection. In Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health), pages 158–168, Albuquerque, New Mexico. Association for Computational Linguistics.