共情的探索：评估小型语言模型在PTSD对话支持中的表现
The Pursuit of Empathy: Evaluating Small Language Models for PTSD Dialogue Support
AI总结：
本文通过构建TIDE数据集，评估0.5B-5B参数的小型语言模型在PTSD对话支持中的共情能力，发现微调可提升其表现，部分小型模型接近人类评分，为资源高效的心理健康支持系统提供了数据与方法基础。
摘要：
本文研究了小型语言模型（0.5B-5B参数）为PTSD患者生成共情回应的能力。我们引入了创伤知情共情对话（TIDE）数据集，这是一个包含10000个两轮对话的新型数据集，覆盖500个多样化、临床基础的PTSD角色（https://huggingface.co/datasets/yenopoya/TIDE）。以前沿模型输出为真实标签，我们在零样本设置和微调后评估了8个小型LLM。微调增强了共情能力，提高了余弦相似度和感知共情，尽管在不同情绪场景中增益各异，且较小模型存在“知识迁移天花板”。正如预期，Claude Sonnet 3.5持续优于所有模型，但令人惊讶的是，小型模型的共情水平常接近人类评分。人口统计分析显示，老年人偏好先验证痛苦再提供支持的回应（p = .004），而研究生学历用户在特定场景中偏好情感层次丰富的回复。性别差异极小（p > 0.15），表明广泛共情的模型设计具有可行性。这项工作为构建资源高效、情感智能的心理健康支持系统提供了见解。
英文摘要：
This paper investigates the capacity of small language models (0.5B-5B parameters) to generate empathetic responses for individuals with PTSD. We introduce Trauma-Informed Dialogue for Empathy (TIDE), a novel dataset comprising 10,000 two-turn conversations across 500 diverse, clinically-grounded PTSD personas (https://huggingface.co/datasets/yenopoya/TIDE). Using frontier model outputs as ground truth, we evaluate eight small LLMs in zero-shot settings and after fine-tuning. Fine-tuning enhances empathetic capabilities, improving cosine similarity and perceived empathy, although gains vary across emotional scenarios and smaller models exhibit a "knowledge transfer ceiling." As expected, Claude Sonnet 3.5 consistently outperforms all models, but surprisingly, the smaller models often approach human-rated empathy levels. Demographic analyses showed that older adults favored responses that validated distress before offering support (p = .004), while graduate-educated users preferred emotionally layered replies in specific scenarios. Gender-based differences were minimal (p > 0.15), suggesting the feasibility of broadly empathetic model designs. This work offers insights into building resource-efficient, emotionally intelligent systems for mental health support.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
