Development of Large Language Model Specialized into Microbiome Datasets: an Application of Self-Evaluation and Scoring Comparison with Conventional Natural Language Processing Markers

Journal of Microbiology and Biotechnology 2026 Open access

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Authors: Chankyu Park, Sung Hwan Bae, Hyeon Woo Park, Nam Su Oh, Young Jun Kim, Young-Wan Kim, Tae Jin Cho, Ying Li, Jianmin Chai, Jiangchao Zhao, Hyung Taek Cho, Ji Hoon Jung
+3 more Jinbong Park, Tae Gyun Kim, Jae Kyeom Kim
DOI: 10.4014/jmb.2511.11050
Cited by: 0

Abstract

< 0.0001) between traditional NLP metrics (BLEU, ROUGE) and human expert rubric scores, with a similar trend observed for correlations with Phi-4-based automated evaluation scores, indicating the limitations of surface-level similarity measures in biomedical contexts. Overall, our findings demonstrate that microbiome-adapted LLMs can effectively distill high-volume scientific data into biologically meaningful insights, supporting more efficient and interpretable research in microbiology and systems biology.

Fields of Study & Subjects

Biomedical Text Mining and Ontologies Topic Modeling Natural Language Processing Techniques

Publication details

Journal / source
Journal of Microbiology and Biotechnology
Volume
36
Pages
e2511050–e2511050
Publication date
26 January 2026
Language
EN