用大模型生成心理化对话,评估其语言结构是否符合临床标准。
The Linguistic Architecture of Reflective Thought: Evaluation of a Large Language Model as a Tool to Isolate the Formal Structure of Mentalization
- 让大模型模拟人类对话,生成心理化文本。
- 评分显示语言结构整体一致,但情感倾向偏中性。
- 适合心理学研究与临床辅助工具开发人员参考。
背景:心理化整合了认知、情感和主体间性成分。大型语言模型(LLMs)表现出生成反思性文本的日益增强能力,引发了语言形式与心理表征关系的疑问。本研究评估单一LLM在标准模式下生成的心理化语言结构与心理化为基础治疗(MBT)参数的一致性。方法:生成50组人类参与者与该模型的对话,五名接受过MBT培训的精神科医生在盲态下依据四个MBT维度对模型生成的心理化谱进行评分,采用李克特量表评估评价连贯性、论证连贯性和整体质量。评分者间一致性通过ICC(3,1)估算。结果:平均分(3.63-3.98)与中等标准差表明生成谱具有高度结构连贯性;ICC值(0.60-0.84)显示评分者间存在实质性至高水平的一致性。模型在隐含-显性及自我-他人维度表现更稳定,但在内在状态与外部情境整合方面存在局限。生成的谱具有一致性且临床可解释,但表现出情感中立特征。
原文摘要 · Abstract (English)
Background: Mentalization integrates cognitive, affective, and intersubjective components. Large Language Models (LLMs) display an increasing ability to generate reflective texts, raising questions regarding the relationship between linguistic form and mental representation. This study assesses the extent to which a single LLM can reproduce the linguistic structure of mentalization according to the parameters of Mentalization-Based Treatment (MBT). Methods: Fifty dialogues were generated between human participants and an LLM configured in standard mode. Five psychiatrists trained in MBT, working under blinded conditions, evaluated the mentalization profiles produced by the model along the four MBT axes, assigning Likert-scale scores for evaluative coherence, argumentative coherence, and global quality. Inter-rater agreement was estimated using ICC(3,1). Results: Mean scores (3.63-3.98) and moderate standard deviations indicate a high level of structural coherence in the generated profiles. ICC values (0.60-0.84) show substantial-to-high agreement among raters. The model proved more stable in the Implicit-Explicit and Self-Other dimensions, while presenting limitations in the integration of internal states and external contexts. The profiles were coherent and clinically interpretable yet characterized by affective neutrality.
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