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经过预先训练的大型语言模型的临床疗效通过语障的透视来看

Yan Cong¹, Arianna N LaCroix², Jiyeon Lee²

¹School of Languages and Cultures, Purdue University, West Lafayette, USA. cong4@purdue.edu.

Scientific reports

|July 6, 2024

概括

大型语言模型 (LLM) 通过识别语言缺陷,在语盲研究中表现有前途. 从LLM衍生出的surprisals有效地诊断,亚型,并预测失言症,推进自动分析.

科学领域:

计算语言学计算语言学
神经科学是一个神经科学.
语音语言病理学语言病理学

背景情况:

大型语言模型 (LLM) 是先进的自然语言处理系统.
修辞障碍研究旨在了解和诊断语言障碍.
在失言症研究中现有的语言索引可能无法完全捕捉所有语言细微差别.

研究的目的:

探索LLMs在语障研究中的实用性.
从LLMs中获得新的语言索引,用于语障分析.
评估LLM作为诊断和测量语言障碍的工具.

主要方法:

使用LLMs的平均惊喜量构建了预测和相关模型.
利用AphasiaBank的存档数据进行模型验证.
作为预测变量,使用了LLM衍生的惊喜值.

主要成果:

在LLM-surprisals中,有效地检测出口障碍的存在和性质.
当与现有的语言索引相结合时,LLM 改善了失语亚型.
在LLM-surprisals中,在单词和句子层面上捕获了语法缺陷.

结论:

简单的语言学习具有提高自动和精确的语障预测的巨大潜力.
将LLM整合到自然语言处理管道中可以改进诸如失语症等语言障碍的模型.
从LLM衍生出的语言指数提供了一种新的口音障碍评估方法.

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相关概念视频

Language and Cognition

Language and Cognition

Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.