用AI分析难民儿童心理数据，提升救助精准度
Harnessing AI Agents to Advance Research on Refugee Child Mental Health
- 构建AI框架处理非结构化难民健康数据
- DeepSeek R1在答案相关性上达0.91，优于Zephyr
- 适合政策制定者与人道机构快速获取关键洞察
全球难民危机持续加剧，数百万流离失所儿童面临严重心理创伤。本研究提出一种紧凑的AI框架，用于处理非结构化难民健康数据并提炼儿童心理健康知识。对比了Zephyr-7B-beta与DeepSeek R1-7B两种检索增强生成（RAG）管道，在处理复杂人道主义数据集时的表现，并评估其幻觉风险。结合前沿AI技术、移民研究与儿童心理学，本研究为政策制定者、心理健康从业者及人道组织提供可扩展的策略，以更好支持流离失所儿童并识别其心理福祉。实验显示，两种模型均有效运行，但DeepSeek R1显著优于Zephyr，答案相关性达到0.91。
原文摘要 · Abstract (English)
The international refugee crisis deepens, exposing millions of dis placed children to extreme psychological trauma. This research suggests a com pact, AI-based framework for processing unstructured refugee health data and distilling knowledge on child mental health. We compare two Retrieval-Aug mented Generation (RAG) pipelines, Zephyr-7B-beta and DeepSeek R1-7B, to determine how well they process challenging humanitarian datasets while avoid ing hallucination hazards. By combining cutting-edge AI methods with migration research and child psychology, this study presents a scalable strategy to assist policymakers, mental health practitioners, and humanitarian agencies to better assist displaced children and recognize their mental wellbeing. In total, both the models worked properly but significantly Deepseek R1 is superior to Zephyr with an accuracy of answer relevance 0.91
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。