用大模型困惑度检测阿尔茨海默病，准确率提升6.35%。
Alzheimer's Dementia Detection Using Perplexity from Paired Large Language Models
- 基于双模型困惑度对比，利用Mistral-7B指令跟随版分析语言模式。
- 在ADReSS 2020基准上准确率提升6.35%，优于现有最佳方法。
- 结果可解释性强，且能捕捉患者特异性语言特征，适合临床辅助诊断。
阿尔茨海默病（AD）是一种以认知衰退为主、常影响语言能力的神经退行性疾病。本文将双模型困惑度方法扩展至使用近期的大语言模型（LLM），即Mistral-7B的指令遵循版本，用于检测AD。相比当前最优的双困惑度方法，准确率平均提升3.33%；相较于ADReSS 2020挑战赛的顶尖方法，提升达6.35%。进一步分析表明，该方法能构建清晰可解释的决策边界，优于其他依赖黑箱决策过程的方法。通过提示微调后的LLM并对比其生成内容与人类响应，我们发现模型已习得AD患者的特殊语言模式，为模型解释与数据增强提供了新路径。
原文摘要 · Abstract (English)
Alzheimer's dementia (AD) is a neurodegenerative disorder with cognitive decline that commonly impacts language ability. This work extends the paired perplexity approach to detecting AD by using a recent large language model (LLM), the instruction-following version of Mistral-7B. We improve accuracy by an average of 3.33% over the best current paired perplexity method and by 6.35% over the top-ranked method from the ADReSS 2020 challenge benchmark. Our further analysis demonstrates that the proposed approach can effectively detect AD with a clear and interpretable decision boundary in contrast to other methods that suffer from opaque decision-making processes. Finally, by prompting the fine-tuned LLMs and comparing the model-generated responses to human responses, we illustrate that the LLMs have learned the special language patterns of AD speakers, which opens up possibilities for novel methods of model interpretation and data augmentation.
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