标题： MetaBench：代谢组学中评估大语言模型的多任务基准 显示英文标题

标题： MetaBench: A Multi-task Benchmark for Assessing LLMs in Metabolomics

摘要： 大型语言模型（LLMs）在通用文本上表现出色；然而，它们在需要深度和相互关联知识的专门科学领域中的能力仍 largely 未被描述。 代谢组学面临着独特的挑战，包括复杂的生化途径、异构的标识符系统和碎片化的数据库。 为了系统评估该领域的LLM能力，我们引入了MetaBench，这是第一个用于代谢组学评估的基准。 从权威的公开资源中整理而来，MetaBench评估了代谢组学研究中五个关键能力：知识、理解、定位、推理和研究。 我们对25个开源和闭源LLM的评估揭示了在代谢组学任务中的不同性能模式：尽管模型在文本生成任务中表现良好，但跨数据库标识符定位即使在检索增强的情况下仍然具有挑战性。 对于注释稀疏的长尾代谢物，模型性能也会下降。 通过MetaBench，我们提供了开发和评估代谢组学AI系统的必要基础设施，推动可靠计算工具在代谢组学研究中的系统性进展。 显示英文摘要

摘要： Large Language Models (LLMs) have demonstrated remarkable capabilities on general text; however, their proficiency in specialized scientific domains that require deep, interconnected knowledge remains largely uncharacterized. Metabolomics presents unique challenges with its complex biochemical pathways, heterogeneous identifier systems, and fragmented databases. To systematically evaluate LLM capabilities in this domain, we introduce MetaBench, the first benchmark for metabolomics assessment. Curated from authoritative public resources, MetaBench evaluates five capabilities essential for metabolomics research: knowledge, understanding, grounding, reasoning, and research. Our evaluation of 25 open- and closed-source LLMs reveals distinct performance patterns across metabolomics tasks: while models perform well on text generation tasks, cross-database identifier grounding remains challenging even with retrieval augmentation. Model performance also decreases on long-tail metabolites with sparse annotations. With MetaBench, we provide essential infrastructure for developing and evaluating metabolomics AI systems, enabling systematic progress toward reliable computational tools for metabolomics research.