Delta-KNN: 提高阿尔茨海默病检测中基于上下文学习的演示选择
Delta-KNN: Improving Demonstration Selection in In-Context Learning for Alzheimer's Disease Detection
AI总结：
Delta-KNN通过Delta评分和KNN检索器提升基于上下文学习的阿尔茨海默病检测性能，实现新状态的最先进结果。
摘要：
阿尔茨海默病（AD）是一种进行性神经退行性疾病，会导致痴呆，早期干预可以通过分析语言异常受益。在本工作中，我们探讨了使用基于上下文学习（ICL）的大型语言模型（LLMs）作为健康助手，从患者生成的文本中进行AD诊断的潜力，其中任务通过几个输入-输出示例定义。实证结果表明，传统ICL方法，如基于相似度的选择，对于AD诊断表现不佳，可能由于该任务固有的复杂性。为了解决这个问题，我们引入了Delta-KNN，一种新的演示选择策略，以提高ICL性能。我们的方法利用delta分数来评估每个训练示例的相对收益，并结合基于KNN的检索器，动态选择最佳的“代表”以适应给定的输入。在两个AD检测数据集上，跨三个开源LLMs的实验表明，Delta-KNN一致优于现有ICL基线。值得注意的是，当使用Llama-3.1模型时，我们的方法实现了新的最先进结果，甚至超过了监督分类器。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
