CurateGPT：一种灵活的语言模型辅助生物编目工具
CurateGPT: A flexible language-model assisted biocuration tool
AI总结：
针对生物医学数据编目耗时、人工产能不足的问题，研究团队推出LLM驱动的工具CurateGPT，结合生成式AI与可信知识库，借助智能体扩展信息获取范围并提供溯源链接，提升编目效率与协作能力。
摘要：
有效的数据驱动生物医学发现离不开数据编目：这是一个耗时的过程，需要查找、整理、提炼、整合、解读、标注并验证各类信息，将其转化为适合数据库与知识库的结构化形式。准确高效地编目这些数字资产，是确保其具备FAIR（可查找、可访问、可互操作、可重用）特性、可信度与可持续性的关键。遗憾的是，专业编目人员面临着严重的时间与资源限制，每日新增发表信息的速度已超出其编目能力。以指令微调大语言模型（LLM）为代表的生成式AI，为辅助人工驱动的编目工作开辟了新可能。智能体的设计理念将生成式AI的新兴能力与更精准的方法相结合。编目人员的任务可借助智能体完成推理、本体检索以及跨外部来源的知识整合，这些工作原本都需要大量人工投入。我们推出的LLM驱动标注工具CurateGPT，将生成式AI的能力与可信知识库、文献资源相融合。CurateGPT简化了编目流程，提升了常见工作流中的协作效率。与直接和LLM交互相比，CurateGPT的智能体能够获取LLM训练数据之外的信息，还能为每条论断提供支撑数据的直接链接。这有助于编目人员、研究人员与工程师扩大编目工作规模，跟上科学数据量持续增长的步伐。
英文摘要：
Effective data-driven biomedical discovery requires data curation: a time-consuming process of finding, organizing, distilling, integrating, interpreting, annotating, and validating diverse information into a structured form suitable for databases and knowledge bases. Accurate and efficient curation of these digital assets is critical to ensuring that they are FAIR, trustworthy, and sustainable. Unfortunately, expert curators face significant time and resource constraints. The rapid pace of new information being published daily is exceeding their capacity for curation. Generative AI, exemplified by instruction-tuned large language models (LLMs), has opened up new possibilities for assisting human-driven curation. The design philosophy of agents combines the emerging abilities of generative AI with more precise methods. A curator's tasks can be aided by agents for performing reasoning, searching ontologies, and integrating knowledge across external sources, all efforts otherwise requiring extensive manual effort. Our LLM-driven annotation tool, CurateGPT, melds the power of generative AI together with trusted knowledge bases and literature sources. CurateGPT streamlines the curation process, enhancing collaboration and efficiency in common workflows. Compared to direct interaction with an LLM, CurateGPT's agents enable access to information beyond that in the LLM's training data and they provide direct links to the data supporting each claim. This helps curators, researchers, and engineers scale up curation efforts to keep pace with the ever-increasing volume of scientific data.
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。
