When Does Bigger Help? A Controlled Study of LLM Scale for Ontology Learning
The effect of Large Language Model (LLM) scale on ontology learning (OL) performance remains insufficiently characterized. We present a controlled evaluation of 13 models spanning dense and Mixture-of-Experts variants from the Qwen3.5 and Qwen3.6 lineages, together with proprietary GPT release variants, using the OntoLearner retrieval-augmented generation pipeline. All models are evaluated with the same embedding model, retrieval configuration, prompt templates, decoding settings, datasets, and metrics on term typing, taxonomy discovery, and non-taxonomic relationship extraction across four bi
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- PossiblePossibly related (embedding) · 48%RAGless: Q-Q retrieval with score aggregation for closed-domain FAQ [P] →
- LinkedLinked via arxiv author · 85%Hamed Babaei Giglou →
“When Does Bigger Help? A Controlled Study of LLM Scale for Ontology Learning”
- LinkedLinked via arxiv author · 85%Sören Auer →
“When Does Bigger Help? A Controlled Study of LLM Scale for Ontology Learning”
- LinkedLinked via arxiv author · 85%Jennifer D'Souza →
“When Does Bigger Help? A Controlled Study of LLM Scale for Ontology Learning”
- FuzzySimilar title/name (fuzzy) · 84%amitness/learning →
“Fuzzy title match (0.92): “When Does Bigger Help? A Controlled Study of LLM Scale for O” ≈ “amitness/learning””