用大模型自动识别访谈中的深层心理冲突，提升精神分析诊断效率。
AutoPsyC: Automatic Recognition of Psychodynamic Conflicts from Semi-structured Interviews with Large Language Models
- 结合参数高效微调与检索增强生成，处理90分钟完整访谈。
- 在141份访谈上对4类心理冲突识别准确率超基线模型。
- 适合临床心理学研究者和精神健康AI开发者参考。
心理动力冲突是影响个体行为与体验的长期、常无意识的主题。准确诊断此类冲突对治疗至关重要，传统上依赖耗时的手动评分半结构化访谈。现有自动化方法多聚焦于抑郁症等宽泛障碍类别，尚不清楚是否能从对话中自动识别患者自身也未必觉察的心理冲突。本文提出AutoPsyC，首个基于大语言模型（LLMs）从完整操作化心理动力诊断（OPD）访谈中识别心理冲突存在性与重要性的方法。该方法融合参数高效微调与检索增强生成（RAG），并采用摘要策略，有效处理长达90分钟的对话内容。在包含141个诊断访谈的数据集上评估显示，AutoPsyC在四类关键心理冲突识别任务中持续优于所有基线与消融实验设置。
原文摘要 · Abstract (English)
Psychodynamic conflicts are persistent, often unconscious themes that shape a person's behaviour and experiences. Accurate diagnosis of psychodynamic conflicts is crucial for effective patient treatment and is commonly done via long, manually scored semi-structured interviews. Existing automated solutions for psychiatric diagnosis tend to focus on the recognition of broad disorder categories such as depression, and it is unclear to what extent psychodynamic conflicts which even the patient themselves may not have conscious access to could be automatically recognised from conversation. In this paper, we propose AutoPsyC, the first method for recognising the presence and significance of psychodynamic conflicts from full-length Operationalized Psychodynamic Diagnostics (OPD) interviews using Large Language Models (LLMs). Our approach combines recent advances in parameter-efficient fine-tuning and Retrieval-Augmented Generation (RAG) with a summarisation strategy to effectively process entire 90 minute long conversations. In evaluations on a dataset of 141 diagnostic interviews we show that AutoPsyC consistently outperforms all baselines and ablation conditions on the recognition of four highly relevant psychodynamic conflicts.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。