Benchmarking Resource-Efficient LLMs for Research Topic Ontology Generation in the Biomedical Field

• URL: http://arxiv.org/abs/2607.17902v1
• Date: Mon, 20 Jul 2026 12:48:41 GMT
• Title: Benchmarking Resource-Efficient LLMs for Research Topic Ontology Generation in the Biomedical Field
• Abstract summary: We assess the performance of five small, open-source Large Language Models (LLMs) in identifying semantic relationships between biomedical concepts.<n>We analyse three adaptation strategies: standard prompting, Chain-of-Thought, and fine-tuning.<n>These results confirm that direct fine-tuning effectively exceeds the reasoning bottlenecks of smaller LLMs.
• Score: 0.8633013637160062
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Knowledge Organization Systems like Ontologies and taxonomies are fundamental for structuring scientific knowledge, yet their manual curation presents a persistent bottleneck in knowledge management. While Large Language Models (LLMs) offer a scalable mechanism for automated ontology generation, their capacity to classify complex, domain-specific semantics requires systematic evaluation. In this paper, we assess the performance of five small, open-source LLMs (up to 9 billion parameters) in identifying semantic relationships between biomedical concepts. To support this evaluation, we introduce MeSH-Rel-4K, a dataset comprising 4K semantic relationships extracted from the Medical Subject Headings (MeSH). We analyse three adaptation strategies: standard prompting, Chain-of-Thought prompting, and fine-tuning. While parameter-constrained models traditionally struggle with the nuances of in-context logic, our results reveal that targeted fine-tuning increases the average F1-score by 34.1 percentage points. These results confirm that direct fine-tuning effectively exceeds the reasoning bottlenecks of smaller LLMs, providing an accurate, automated methodology for the construction and evolution of specialised biomedical ontologies.

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