LLM Embeddings Complement Theory-Driven Language Features and Cognitive Scores for APOE4 and Aβ Prediction

Spontaneous language offers a scalable, noninvasive approach for identifying biological indicators of preclinical Alzheimer's disease (AD). We examined prediction of APOE4 carrier status and cerebral amyloid-beta burden using Cookie Theft picture descriptions from two cohorts of Hebrew- and English-speaking participants (IRAP, N=134; WRAP, N=1,541). Amyloid-beta burden was classified using lowest-quartile, median, and highest-quartile cutoffs, capturing accumulation below conventional amyloid-positivity thresholds. We compared embeddings from pretrained large language models (LLMs) with standa

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