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.

翻译：有效的数据驱动生物医学发现需要数据管理：这是一个耗时的过程，涉及寻找、组织、提炼、整合、解释、注释和验证多样化信息，并将其转化为适合数据库和知识库的结构化形式。对这些数字资产进行准确高效的管理，对于确保其符合FAIR原则（可查找、可访问、可互操作、可重用）、可信且可持续至关重要。然而，专家管理者面临着显著的时间和资源限制。每日发表的新信息速度之快，已超出了他们的管理能力。以指令调优的大语言模型（LLMs）为代表的生成式人工智能，为辅助人类驱动的数据管理开辟了新的可能性。智能体的设计理念将生成式AI的新兴能力与更精确的方法相结合。管理者的任务可以通过智能体辅助进行推理、搜索本体论以及整合跨外部来源的知识，这些工作原本都需要大量的人工努力。我们基于LLM的注释工具CurateGPT，将生成式AI的强大能力与可信的知识库和文献来源相结合。CurateGPT简化了管理流程，增强了常见工作流程中的协作与效率。与直接与LLM交互相比，CurateGPT的智能体能够访问LLM训练数据之外的信息，并为每个主张提供支持数据的直接链接。这有助于管理者、研究人员和工程师扩大管理规模，以跟上科学数据不断增长的步伐。