做生物医学NLP或数据标注的团队，可以用这套方法低成本提升LLM标注质量，值得参考实验设计。
该研究提出一种系统性的标注指南复用与优化框架，通过模拟标注项目早期阶段的迭代审核机制，提升大语言模型在零样本标注任务中的表现。在三个生物医学命名实体识别数据集（NCBI Disease、BC5CDR、BioRED）上，使用GPT、Gemini、DeepSeek三类模型进行测试，验证了指南整合的有效性、推理优化模型的优势以及最小监督下审核的可行性。实验表明该框架能有效优化标注指南，但仍有较大改进空间。
Refining and Reusing Annotation Guidelines for LLM Annotation
While Large Language Models (LLMs) demonstrate remarkable performance on zero-shot annotation tasks, they often struggle with the specialized conventions of gold-standard benchmarks. We propose the systematic reuse and refinement of annotation guidelines as an alignment mechanism, introducing an iterative moderation framework that simulates the early phases of annotation projects. We evaluate three hypotheses: (1) the efficacy of guideline integration, (2) the advantage of reasoning optimized models, and (3) the viability of moderation under minimal supervision. Testing across biomedical NER tasks (NCBI Disease, BC5CDR, BioRED) with three LLM families (GPT, Gemini, DeepSeek), our results empirically confirm all three hypotheses. While the iterative moderation framework shows good potential in effectively refining guidelines, our analysis also reveals substantial room for improvement.