arXiv cs.AIPaper
When Does Bigger Help? A Controlled Study of LLM Scale for Ontology Learning
The headline is scale, but the real finding is domain-specific: bigger doesn't always help, and the relationship isn't linear. For practitioners working on structured extraction or knowledge tasks, this means benchmarking your own models on your own data before assuming parameter count matters. The sweet spot appears to be 27B for dense models rather than chasing the largest option.