Delta-KNN: Improving Demonstration Selection in In-Context Learning for Alzheimer's Disease Detection
Journal:
arXiv
Published Date:
Jun 4, 2025
Abstract
Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that
leads to dementia, and early intervention can greatly benefit from analyzing
linguistic abnormalities. In this work, we explore the potential of Large
Language Models (LLMs) as health assistants for AD diagnosis from
patient-generated text using in-context learning (ICL), where tasks are defined
through a few input-output examples. Empirical results reveal that conventional
ICL methods, such as similarity-based selection, perform poorly for AD
diagnosis, likely due to the inherent complexity of this task. To address this,
we introduce Delta-KNN, a novel demonstration selection strategy that enhances
ICL performance. Our method leverages a delta score to assess the relative
gains of each training example, coupled with a KNN-based retriever that
dynamically selects optimal "representatives" for a given input. Experiments on
two AD detection datasets across three open-source LLMs demonstrate that
Delta-KNN consistently outperforms existing ICL baselines. Notably, when using
the Llama-3.1 model, our approach achieves new state-of-the-art results,
surpassing even supervised classifiers.
