使用大型语言模型用于痴呆症检测的对抗性文本生成
Youxiang Zhu1, Nana Lin1, Kiran Sandilya Balivada1
1University of Massachusetts Boston, Boston, MA, USA.
Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing
|September 18, 2025
概括
大型语言模型 (LLM) 难以从图像中检测痴呆症. 一种名为Adversarial Text Generation (ATG) 的新方法,使用特定任务的指令,提高了超过10%的准确性.
科学领域:
- 人工智能的人工智能
- 认知科学 认知科学
- 医疗信息学 医疗信息学
背景情况:
- 大型语言模型 (LLM) 在文本分类方面表现有前途,但在从图像描述中检测痴呆症等专业任务中面临挑战.
- 由于痴呆症的细微语言标记,标准提示方法是不够的,LLMs难以将内部知识与此特定的诊断任务联系起来.
研究的目的:
- 使用图像描述开发一种准确和可解释的痴呆症检测分类方法.
- 引入一种新的解码策略,即对抗性文本生成 (ATG),以提高在痴呆症检测中LLM的性能.
主要方法:
- 反对文本生成 (ATG) 被用作一种新的解码策略,将痴呆症检测与其他相关任务联系起来.
- 一套全面的特定任务指令被开发并用于指导ATG过程.
- 引入特征上下文是为了为LLM的分类决定提供人类可以理解的解释.
主要成果:
- 拟议的ATG方法在痴呆症检测中达到85%的最高准确性.
- 与传统的提示策略相比,这代表了超过10%的显著改善.
- 功能上下文分析显示,痴呆症检测与评估细节,语言和清晰度的注意力相关,与环境和特征特征相关.
结论:
- 与标准提示相比,Adversarial Text Generation (ATG) 提供了一种更准确,更易于解释的方法来使用LLM检测痴呆症.
- 功能上下文为LLM的决策过程提供了有价值的见解,突出了痴呆症的关键歧视性特征.
- 未来的研究将探索多模式的LLM,以整合语音和视觉信息,以加强痴呆症评估.
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