构建神经学语言模型
Building Models of Neurological Language
AI总结：
面向神经学领域，项目从定制模型转向基于RAG与表征模型的本地安全部署，构建专用数据集、术语图分析工具及Docker托管方案。
摘要：
本报告记录了神经学领域专用语言模型的开发与评估。项目最初聚焦于构建定制模型，随后适应开源及商业医学LLM的快速发展，转向利用检索增强生成（RAG）和表征模型，以实现安全的本地部署。主要贡献包括创建神经学专用数据集（病例报告、问答集、教材衍生数据）、多词表达提取工具，以及基于图的医学术语分析。项目还制作了用于本地托管的脚本和Docker容器。报告汇报了性能指标和图社区结果，并指出未来可开展使用phi-4等开源架构的多模态模型研究。
英文摘要：
This report documents the development and evaluation of domain-specific language models for neurology. Initially focused on building a bespoke model, the project adapted to rapid advances in open-source and commercial medical LLMs, shifting toward leveraging retrieval-augmented generation (RAG) and representational models for secure, local deployment. Key contributions include the creation of neurology-specific datasets (case reports, QA sets, textbook-derived data), tools for multi-word expression extraction, and graph-based analyses of medical terminology. The project also produced scripts and Docker containers for local hosting. Performance metrics and graph community results are reported, with future possible work open for multimodal models using open-source architectures like phi-4.
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。
