用新方法选示范样本，让大模型更准识别阿尔茨海默病
Delta-KNN: Improving Demonstration Selection in In-Context Learning for Alzheimer's Disease Detection
- 基于增量得分和KNN动态筛选最有代表性的示范样本
- 在两个数据集上超越现有方法，Llama-3.1达最优性能
- 适合做医疗文本诊断的AI研究者与临床辅助系统开发者
阿尔茨海默病（AD）是一种进行性神经退行性疾病，早期干预可通过分析语言异常实现。本文探索利用大语言模型（LLM）通过上下文学习（ICL）从患者自述文本中进行AD诊断，即通过少量输入输出样例定义任务。实验表明，传统基于相似度的示范选择方法在该任务上表现不佳，可能源于任务复杂性。为此，我们提出Delta-KNN，一种新型示范选择策略：利用增量得分评估每个训练样本的相对增益，并结合KNN检索器动态选择最优“代表性”示范。在两个AD检测数据集上，使用三种开源LLM的实验显示，Delta-KNN始终优于现有ICL基线。尤其在Llama-3.1模型上，达到新基准性能，甚至超过监督分类器。
原文摘要 · Abstract (English)
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.
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