对比BERT与大模型在文本分类中的表现，发现前者在多数任务上仍更优。
Do BERT-Like Bidirectional Models Still Perform Better on Text Classification in the Era of LLMs?
- 对比微调BERT、利用大模型内部状态、零样本推理三种方法
- 在六个高难度数据集上，BERT类模型多数情况下优于大模型
- 提出按任务类型选择模型的策略，适合需要精细匹配的场景
大模型的快速普及掩盖了传统BERT类模型在文本分类中的潜力。本研究通过系统比较三种方法——BERT类模型微调、大模型内部状态利用、零样本推理，在六个高难度数据集上展开评估。结果表明，BERT类模型常优于大模型。我们进一步将数据集分为三类，通过PCA与探针实验识别出：BERT类模型在模式驱动任务中表现优异，而大模型在需要深层语义或世界知识的任务中占优。基于此，提出细粒度任务选择策略TaMAS，倡导根据任务特性选择模型，而非盲目依赖大模型。
原文摘要 · Abstract (English)
The rapid adoption of LLMs has overshadowed the potential advantages of traditional BERT-like models in text classification. This study challenges the prevailing "LLM-centric" trend by systematically comparing three category methods, i.e., BERT-like models fine-tuning, LLM internal state utilization, and zero-shot inference across six high-difficulty datasets. Our findings reveal that BERT-like models often outperform LLMs. We further categorize datasets into three types, perform PCA and probing experiments, and identify task-specific model strengths: BERT-like models excel in pattern-driven tasks, while LLMs dominate those requiring deep semantics or world knowledge. Based on this, we propose TaMAS, a fine-grained task selection strategy, advocating for a nuanced, task-driven approach over a one-size-fits-all reliance on LLMs.
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