A Comparative Study on the Performance of Generative and Discriminative Models in Text Multi-Classification Tasks under Similar Model Sizes

针对自然语言处理中应用广泛的文本多分类任务，为了探究其在实际落地过程中最合适的模型选型。本文从预测准确率和输出响应时延两个维度，对尺寸大小相近的生成式模型Qwen3-0.6b和判别式模型Bert-base在文本多分类任务上的性能表现进行了对比研究。作者使用了THUCNews新闻数据集，设置了Bert微调、Qwen3-0.6b零样本提示、Qwen3-0.6b全参微调和Qwen3-0.6bLoRA微调四组对比实验。实验表明，在相同训练样本的条件下，判别式模型在宏观平均F1值和预测速度上都优于最好的生成式模型方案，分别高出了0.8%和356.8%。In the widely applied text multi-classification task of natural language processing, this paper aims to explore the most suitable model selection for practical implementation. From the perspectives of prediction accuracy and response latency, we conducted a comparative study on the performance of the generative model Qwen3-0.6b and the discriminative model Bert-base in text multi-classification tasks. The experiments were based on the THUCNews news dataset, with four comparison groups set up: Bert fine-tuning, Qwen3-0.6b zero-shot prompting, Qwen3-0.6b full-parameter fine-tuning, and Qwen3-0.6b LoRA fine-tuning. The results demonstrated that, under the same training data conditions, the discriminative model outperformed the best-performing generative model approach in both macro-average F1 score and prediction speed, achieving improvements of 0.8% and 356.8%, respectively.