通过预先训练的语言模型跟踪化诱导的语言恢复
Yan Cong1,2, Jiyeon Lee3
1School of Languages and Cultures, Purdue University, West Lafayette, IN, United States.
Frontiers in artificial intelligence
|November 17, 2025
概括
预训练的语言模型 (PLMs) 有效地跟踪经过初始治疗的失语患者的语言恢复. 随着治疗,PLM惊喜分数下降,显示出临床康复和个性化跟踪的前景.
科学领域:
- 计算语言学计算语言学
- 神经康复疗法 神经康复疗法
- 人工智能在医学中的应用
背景情况:
- 脑卒中后的语言障碍阿法西亚在追踪恢复方面存在挑战.
- 目前用于评估在失言症中语言恢复的方法可能是主观的和耗时的.
- 预训练语言模型 (PLM) 为语言分析提供了新的计算方法.
研究的目的:
- 评估PLM衍生突发的实用性,以衡量听治疗后的语言变化的语言变化.
- 开发和评估基于提示的管道,使用PLM来自动化在失语症中的临床分类任务.
- 探索生成AI (GenAI) 在临床康复环境中用于语言恢复的潜力.
主要方法:
- 利用PLM衍生的惊喜分数 (负日志概率) 来量化语言的复杂性和变化.
- 分析了因失语而接受结构性原始治疗的参与者的惊喜得分变化.
- 开发并验证了一个基于提示的管道,用于分类语音失语中的句子正确性和错误类型.
主要成果:
- 在结构原始化治疗后,惊喜分数显著下降,这表明语言有所改善.
- 在严重句子制作障碍的参与者中,surprisal的减少更为明显.
- 基于提示的管道在分类失语句的正确性 (F1=0.967) 和错误检测 (准确性=0.846) 中取得了高性能.
结论:
- 由PLM衍生的突发反应为跟踪治疗诱导的语言恢复提供了连续和可解释的测量方法.
- PLM显示了语言康复的自动化临床分类和建模的巨大潜力.
- 这项研究突出了GenAI在临床环境中的实际应用,用于个性化失言症治疗和进度监测.
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