SOURCE-LINKED INTELLIGENCE
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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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-31T17:30:05.000Z
First collected: 2026-09-21T06:41:57.136Z. This is not the publication date.