超越普通示例：面向阿尔茨海默病检测的以示例为中心锚定上下文学习范式
Beyond Plain Demos: A Demo-centric Anchoring Paradigm for In-Context Learning in Alzheimer's Disease Detection
AI总结：
针对阿尔茨海默病检测中上下文学习示例同质、信号不足的问题，提出DA4ICL框架，通过多样对比检索扩展上下文宽度、投影向量锚定加深信号，在三个基准上显著超越ICL和任务向量基线。
AI中文摘要：
从叙事转录文本中检测阿尔茨海默病（AD）对大语言模型（LLMs）构成挑战：预训练很少覆盖这一分布外任务，且所有转录示例描述同一场景，产生高度同质化的上下文。这些因素既削弱了模型内置的任务知识（任务认知），也削弱了其浮现细微、类别区分性线索的能力（上下文感知）。由于认知在预训练后固定，改进AD检测的上下文学习（ICL）依赖于通过更好的示例集来增强感知。我们证明标准ICL会迅速饱和，其示例缺乏多样性（上下文宽度）且无法传递细粒度信号（上下文深度），而近期的任务向量（TV）方法虽然通过向LLMs的隐藏状态（HSs）注入TV来改善广泛任务适应，但由于注入粒度、强度和位置不匹配，并不适合AD检测。为解决这些瓶颈，我们提出DA4ICL，一个以示例为中心的锚定框架，通过多样对比检索（DCR）联合扩展上下文宽度，并在每个Transformer层通过投影向量锚定（PVA）加深每个示例的信号。在三个AD基准上，DA4ICL相比ICL和TV基线均取得大幅且稳定的提升，为细粒度、分布外和低资源LLM适应开辟了新范式。
英文摘要：
Detecting Alzheimer's disease (AD) from narrative transcripts challenges large language models (LLMs): pre-training rarely covers this out-of-distribution task, and all transcript demos describe the same scene, producing highly homogeneous contexts. These factors cripple both the model's built-in task knowledge (\textbf{task cognition}) and its ability to surface subtle, class-discriminative cues (\textbf{contextual perception}). Because cognition is fixed after pre-training, improving in-context learning (ICL) for AD detection hinges on enriching perception through better demonstration (demo) sets. We demonstrate that standard ICL quickly saturates, its demos lack diversity (context width) and fail to convey fine-grained signals (context depth), and that recent task vector (TV) approaches improve broad task adaptation by injecting TV into the LLMs' hidden states (HSs), they are ill-suited for AD detection due to the mismatch of injection granularity, strength and position. To address these bottlenecks, we introduce \textbf{DA4ICL}, a demo-centric anchoring framework that jointly expands context width via \emph{\textbf{Diverse and Contrastive Retrieval}} (DCR) and deepens each demo's signal via \emph{\textbf{Projected Vector Anchoring}} (PVA) at every Transformer layer. Across three AD benchmarks, DA4ICL achieves large, stable gains over both ICL and TV baselines, charting a new paradigm for fine-grained, OOD and low-resource LLM adaptation.
