Qwen团队研究了13个模型规模对本体学习的影响，发现参数量并非越大越好，架构和模型血统比参数数量更重要。
研究评估了13个模型，包括Qwen3.5和Qwen3.6系列以及GPT变体，使用OntoLearner管道。在Qwen3.5密集型模型中，参数量增加主要提高精确率而非召回率，9B到27B参数间提升最大。27B密集型模型在术语分类上显著优于更大的稀疏模型，而更大的MoE模型在分类发现上表现最佳。非分类关系提取在所有模型规模上仍具挑战性，特别是在材料数据科学本体上。
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 biomedical and materials science and engineering ontologies. Within the dense Qwen3.5 lineage, increasing parameter count primarily improves precision rather than recall, with the largest gains occurring between 9B and 27B parameters. However, the effect of scale is neither monotonic nor uniform across tasks and domains. Dense 27B models outperform substantially larger sparse models on term typing, whereas larger Mixture-of-Experts models achieve the strongest open-weight results on taxonomy discovery. Non-taxonomic relationship extraction remains difficult across model scales, particularly for the Materials Data Science ontology. Performance differences across matched Qwen variants and proprietary GPT releases further indicate that architecture and model lineage can outweigh nominal parameter count. These findings show that model size alone is an insufficient selection criterion for OL and provide empirical guidance for reproducible LLM-assisted ontology engineering.