困惑性和接近性:大型语言模型的困惑性补充了语义距离指标,用于检测不连贯的语音
Weizhe Xu1, Serguei Pakhomov2, Patrick Heagerty3
1Biomedical Informatics and Medical Education (BIME), University of Washington, Seattle, WA, USA.
Journal of biomedical informatics
|August 23, 2025
概括
这项研究引入了使用大型语言模型 (LLM) 测量语音连贯性的新方法,有助于诊断精神分裂症谱系障碍 (SSD). 结合LLM和传统指标, 提高了评估无组织思维的准确性.
科学领域:
- 计算语言学
- 精神病学
- 机器学习
背景情况:
- 语音中的语义连贯性反映了有组织的思维,对于诊断精神分裂症谱系障碍 (SSD) 至关重要.
- 没有组织的思维是SSD精神病的核心症状.
- 评估语义连贯性的自动化工具可以帮助早期检测和症状监测.
研究的目的:
- 开发基于大型语言模型 (LLM) 的语义连贯性的新方法.
- 将基于LLM的连贯度量与传统的向量距离方法进行比较.
- 评估LLM困惑在SSD中识别无组织思维的有用性.
主要方法:
- 评估了使用LLM复杂性的"袋子"和"链条"模型来实现语义连贯性.
- 训练有素的回归模型结合了困惑和近距离特征来预测人类的一致性等级.
- 从精神病症状的个体和SSD患者的临床采访中验证了语言模型.
主要成果:
- 使用LLM困惑性和近距离特征的综合方法实现了与训练组中人类评分的最高相关性 (0.61).
- 这种结合方法在测试组中单独优于近距离特征 (0.54比0.52).
- 基于LLM的困难度指标证明了对传统的近距离度量的补充价值.
结论:
- 开发并验证了基于LLM的语义连贯性评估新方法.
- 通过将LLM的复杂性与近距离特征相结合,可以提高语音一致性的自动评估.
- 这些发现强调了LLM在改善SSD的诊断和监测方面的潜力.
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