Three species of Laboulbeniales are described from Korea on species of the order Coleoptera. They are; Laboulbenia borealis Spegazzini on Gyrinus japonicus Sharp (Gyrinidae), Laboulbenia humilis Thaxter on Chlaenius naeviger Morawitz (Carabidae) and Laboulbenia benjaminii Balazuc on Stenolophus difficilis Hope (Carabidae).
Instruction Tuning on Large Language Models is an essential process for model to function well and achieve high performance in specific tasks. Accordingly, in mainstream languages such as English, instruction-based datasets are being constructed and made publicly available. In the case of Korean, publicly available models and datasets all rely on using the output of ChatGPT or translating datasets built in English. In this paper, We introduce KIT-19 as an instruction dataset for the development of LLM in Korean. KIT-19 is a dataset created in an instruction format, comprising 19 existing open-source datasets for Korean NLP tasks. In this paper, we train a Korean Pretrained LLM using KIT-19 to demonstrate its effectiveness. The experimental results show that the model trained on KIT-19 significantly outperforms existing Korean LLMs. Based on the its quality and empirical results, this paper proposes that KIT-19 has the potential to make a substantial contribution to the future improvement of Korean LLMs' performance.
Five species of the Laboulbeniales, including two unrecorded species are reported from South Korea. They are as follows; Dioicomyces anthici Thaxter on Anthicus confucii Marseul, Laboulbenia melanaria Thaxter on Anisodacthius tricuspidatus Morawitz, L. philonthi Thaxter on Philonthus wuesthoffi Bernhauer, Peyritschiella japonicus Terada on Philonthus japonicus Sharp, and Scaphidiomyces baeocerae Thaxter on Scaphisoma unicolor Achard. Among these species, L. melanaria Thaxter, S. baeocerae Thaxter and the male thallus of D. anthici Thaxter are newly described from South Korea. L. Philonthi Thaxter and P. japonicus Terada are newly collected in some places where were unlike with the examined region ago.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.