通过对话数据识别心理防御机制水平，助力心理健康评估。
You Never Know a Person, You Only Know Their Defenses: Detecting Levels of Psychological Defense Mechanisms in Supportive Conversations
- 构建对话语料库并设计四阶段标注工具，提升标注效率
- 模型在零样本和微调下最佳宏F1仅30%，显示研究空间大
- 适合心理学、NLP交叉研究者关注防御机制与语言行为
心理防御是人们应对压力的自动策略，过度使用与心理健康不良相关，并影响求助者表达与接受帮助的方式。然而，防御机制复杂且难以在临床对话中可靠测量。本文提出PsyDefConv对话语料库，包含200段对话、4709个话语（含2336个求助者发言），标注一致性为Cohen's kappa 0.639。同时开发DMRS Co-Pilot四阶段流程，使平均标注时间减少22.4%。专家评审显示其证据支持度4.62、临床合理性4.44、洞察力4.40（七分制）。强语言模型在零样本与微调设置下的基准测试显示明显提升空间，最佳宏F1约30%，且倾向于高估成熟型防御。语料分析表明成熟防御最常见，并发现情绪特异性偏差。论文将公开语料、标注、代码与提示模板，推动语言中防御功能的研究。
原文摘要 · Abstract (English)
Psychological defenses are strategies, often automatic, that people use to manage distress. Rigid or overuse of defenses is negatively linked to mental health and shapes what speakers disclose and how they accept or resist help. However, defenses are complex and difficult to reliably measure, particularly in clinical dialogues. We introduce PsyDefConv, a dialogue corpus with help seeker utterances labeled for defense level, and DMRS Co-Pilot, a four-stage pipeline that provides evidence-based pre-annotations. The corpus contains 200 dialogues and 4709 utterances, including 2336 help seeker turns, with labeling and Cohen's kappa 0.639. In a counterbalanced study, the co-pilot reduced average annotation time by 22.4%. In expert review, it averaged 4.62 for evidence, 4.44 for clinical plausibility, and 4.40 for insight on a seven-point scale. Benchmarks with strong language models in zero-shot and fine-tuning settings demonstrate clear headroom, with the best macro F1-score around 30% and a tendency to overpredict mature defenses. Corpus analyses confirm that mature defenses are most common and reveal emotion-specific deviations. We will release the corpus, annotations, code, and prompts to support research on defensive functioning in language.
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