Abstract
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.
Abstract (translated)
生物的学名由属名和种加词组成，后者常反映形态、生态、分布及文化背景等方面的信息。传统上，研究人员通过仔细研究分类描述来手动标注物种名称，处理大规模数据集时需要耗费大量时间和精力。本研究评估了利用大型语言模型（LLM）自动标注物种名称的可行性，这些模型能够利用其文本分类和语义提取能力。我们使用Mammola等人编纂的蜘蛛名称数据集，将基于LLM的标签结果与人工注释进行了比较，并通过提示工程优化了LLM的表现。结果显示，在形态、地理及人类类别中，基于LLM的分类达到了高准确性；然而在生态行为和现代及历史文化类别中的分类准确性较低，显示出了解释动物行为和文化背景方面的挑战。未来的研究将致力于通过优化少样本学习和检索增强生成技术来提高准确度，并扩大基于LLM标签的应用范围至各种生物类群。
URL
https://arxiv.org/abs/2503.10662
