Scientific names of organisms consist of a genus name and a species epithet, with the latter often reflecting aspects such as morphology, ecology, distribution, and cultural background. Traditionally, researchers have manually labeled species names by carefully examining taxonomic descriptions, a process that demands substantial time and effort when dealing with large datasets. This study evaluates the feasibility of automatic species name labeling using large language model (LLM) by leveraging their text classification and semantic extraction capabilities. Using the spider name dataset compiled by Mammola et al., we compared LLM-based labeling results-enhanced through prompt engineering-with human annotations. The results indicate that LLM-based classification achieved high accuracy in Morphology, Geography, and People categories. However, classification accuracy was lower in Ecology & Behavior and Modern & Past Culture, revealing challenges in interpreting animal behavior and cultural contexts. Future research will focus on improving accuracy through optimized few-shot learning and retrieval-augmented generation techniques, while also expanding the applicability of LLM-based labeling to diverse biological taxa.

翻译：生物的科学名称由属名和种加词构成，其中种加词常反映形态、生态、分布及文化背景等特征。传统上，研究者需通过仔细查阅分类学描述来人工标注物种名称，这一过程在处理大规模数据集时需要耗费大量时间和精力。本研究评估了利用语言大模型（LLM）的文本分类与语义提取能力实现物种名称自动标注的可行性。我们使用Mammola等人编制的蜘蛛名称数据集，将通过提示工程优化的LLM标注结果与人工标注进行了比较。结果表明，LLM在形态、地理和人物类别中实现了较高的分类准确率。然而，在生态与行为、现代与历史文化类别中分类准确率较低，这揭示了解读动物行为和文化背景方面存在的挑战。未来研究将侧重于通过优化的小样本学习和检索增强生成技术提升准确率，同时拓展基于LLM的标注方法在多样化生物类群中的适用性。