Automated Skill Decomposition Meets Expert Ontologies: Bridging the Granularity Gap with LLMs

• URL: http://arxiv.org/abs/2510.11313v1
• Date: Mon, 13 Oct 2025 12:03:06 GMT
• Title: Automated Skill Decomposition Meets Expert Ontologies: Bridging the Granularity Gap with LLMs
• Authors: Le Ngoc Luyen, Marie-Hélène Abel,
• Abstract summary: This paper investigates automated skill decomposition using Large Language Models (LLMs)<n>Our framework standardizes the pipeline from prompting and generation to normalization and alignment with ontology nodes.<n>To evaluate outputs, we introduce two metrics: a F1-score that uses optimal embedding-based matching to assess content accuracy, and a hierarchy-aware F1-score that credits structurally correct placements to assess granularity.
• Score: 1.2891210250935148
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This paper investigates automated skill decomposition using Large Language Models (LLMs) and proposes a rigorous, ontology-grounded evaluation framework. Our framework standardizes the pipeline from prompting and generation to normalization and alignment with ontology nodes. To evaluate outputs, we introduce two metrics: a semantic F1-score that uses optimal embedding-based matching to assess content accuracy, and a hierarchy-aware F1-score that credits structurally correct placements to assess granularity. We conduct experiments on ROME-ESCO-DecompSkill, a curated subset of parents, comparing two prompting strategies: zero-shot and leakage-safe few-shot with exemplars. Across diverse LLMs, zero-shot offers a strong baseline, while few-shot consistently stabilizes phrasing and granularity and improves hierarchy-aware alignment. A latency analysis further shows that exemplar-guided prompts are competitive - and sometimes faster - than unguided zero-shot due to more schema-compliant completions. Together, the framework, benchmark, and metrics provide a reproducible foundation for developing ontology-faithful skill decomposition systems.

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