区分吸毒者自我污名类型，用个性化对话提升支持效果。
Self-Stigma Is Not a Monolith, but Generic Empathy Is: Persona-Conditioned LLM Support for People Who Use Drugs
- 基于1174条帖子分析出四种自我污名人格类型
- 30条发言即可准确识别人格，比通用模型高20%以上
- 临床专家更偏爱通用共情，但个性化回应更有效
自我污名会预测吸毒者（PWUD）回避治疗与脱离干预，但现有对话系统通常将自我污名表达视为单一信号。我们开展三阶段概念验证研究，提出一种人格感知的LLM支持方法。对Reddit上1,174名自我污名表达者进行指标级特征的潜在剖面分析（LPA），得出四类人格类型，并通过保留行为与语言特征验证其有效性。序列贝叶斯与循环神经网络分类器在有限发帖历史下可准确恢复人格，显著优于批量与少样本LLM基线（30条帖子时宏平均F1达0.74）。八位临床专家评估三种当代LLM后发现：人格匹配回应能有效引导目标行为转变，但评审者整体更偏好无人格差异的通用共情基线。结果表明，整体共情判断与临床有效设计可能背道而驰，评估基于LLM的污名支持需具备分解两者的评价框架。
原文摘要 · Abstract (English)
Self-stigma predicts treatment avoidance and disengagement among people who use drugs (PWUD), yet conversational systems aiming to provide support typically treat self-stigma expression as a uniform signal. We present a three-phase, proof-of-concept study of a persona-aware approach to LLM support. Latent Profile Analysis (LPA) on indicator-level features from 1,174 self-stigma expressors on Reddit yields a four-persona typology validated against held-out behavioral and linguistic features. Sequential Bayesian and recurrent neural classifiers recover these personas from limited posting histories, substantially outperforming batch and few-shot LLM baselines (macro-F1 = 0.74 at 30 posts). Evaluation by eight clinical experts across three contemporary LLMs revealed a misalignment: persona-matched responses successfully achieved targeted behavioral shifts, yet raters holistically preferred the generic empathy of the persona-neutral baseline. Our findings suggest that holistic empathy judgments and clinically-aligned response design can pull in opposite directions, and that evaluating LLM-based stigma support requires rubrics capable of decomposing the two.
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