以人为中心评估文本到图像生成模型以表达心理压力：基于GPT-4o的数据库
Human-Centred Evaluation of Text-to-Image Generation Models for Self-expression of Mental Distress: A Dataset Based on GPT-4o
AI总结：
本研究基于GPT-4o构建首个公开的文本到图像评估数据集，评估AI生成图像在心理健康自我表达中的有效性，发现角色提示设计显著影响用户评分。
摘要：
有效沟通是实现心理健康领域积极健康结果的核心，但国际学生常面临语言和文化障碍，阻碍他们表达心理压力。在本研究中，我们评估了AI生成图像在支持自我表达心理压力方面的有效性。为此，邀请了20名在中国国际学生在英国大学学习的学生描述他们个人的心理压力经历。这些描述通过GPT-4o用四个基于角色的提示模板进行扩展，这些模板根植于当代辅导实践，生成相应的图像。参与者然后根据他们最初描述评估生成图像在促进他们感受表达方面的帮助程度。所得到的数据集包含100个心理压力描述文本、400张生成图像以及相应的评分。研究发现，提示设计显著影响感知的有用性，其中绘图师角色获得了最高评分。本工作引入了首个公开可用的文本到图像评估数据集，包含心理健康领域的评分，为图像评估、人反馈强化学习和多模态研究提供有价值的资源。
英文摘要：
Effective communication is central to achieving positive healthcare outcomes in mental health contexts, yet international students often face linguistic and cultural barriers that hinder their communication of mental distress. In this study, we evaluate the effectiveness of AI-generated images in supporting self-expression of mental distress. To achieve this, twenty Chinese international students studying at UK universities were invited to describe their personal experiences of mental distress. These descriptions were elaborated using GPT-4o with four persona-based prompt templates rooted in contemporary counselling practice to generate corresponding images. Participants then evaluated the helpfulness of generated images in facilitating the expression of their feelings based on their original descriptions. The resulting dataset comprises 100 textual descriptions of mental distress, 400 generated images, and corresponding human evaluation scores. Findings indicate that prompt design substantially affects perceived helpfulness, with the illustrator persona achieving the highest ratings. This work introduces the first publicly available text-to-image evaluation dataset with human judgment scores in the mental health domain, offering valuable resources for image evaluation, reinforcement learning with human feedback, and multi-modal research on mental health communication.
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。
