预训练的人工智能语言模型代表了自闭症和遗传相关现象型的核心的务实语言变异性
Joseph Cy Lau1, Emily Landau1, Qingcheng Zeng1
1Northwestern University, USA.
Autism : the international journal of research and practice
|December 20, 2024
概括
使用变压器双向编码器表示的人工智能可以识别自闭症和相关疾病中的实用语言差异. 这种自动化方法为研究和临床应用提供了一种有效的替代时间耗费的手动编码方法.
科学领域:
- 计算语言学计算语言学
- 神经发育障碍 神经发育障碍
- 医疗保健中的人工智能
背景情况:
- 自闭症的特点是务实的语言挑战,影响社会沟通.
- 目前用于实用语言的手动编码方法耗时且限制样本大小.
- 自闭症,脆弱X综合征和FMR1前变异之间存在遗传联系,这表明共享的社会语言影响.
研究的目的:
- 为了评估人工智能 (AI) 预训练的语言模型,双向编码器从变压器 (BERT) 的表示,以表征实用语言的变化.
- 评估BERT在区分自闭症个体与非自闭症群体方面的有效性.
- 探索BERT在具有实用差异的遗传相关表型中的实用性.
主要方法:
- 应用BERT的下一个句子预测模块以计算指数实用变量.
- 对自闭症患者,自闭症患者的父母,脆弱X综合征和FMR1前变异携带者的数据进行分析.
- 人工智能衍生的估计与与对话连贯性相关的手动编码实用能力的相关性.
主要成果:
- 在没有微调的情况下,BERT成功地将自闭症与非自闭症组区分开来.
- 计算估计与实用能力的手动编码相关,包括对话连贯性.
- 这些发现在自闭症和遗传相关的表型中观察到.
结论:
- 像BERT这样的AI语言模型显示出对自动化,高效和客观实用性特征的承诺.
- 这种方法可以帮助克服大型研究中手动编码的局限性.
- 人工智能工具有可能推进自闭症和相关沟通挑战的研究和干预策略.
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