Do BERT-Like Bidirectional Models Still Perform Better on Text Classification in the Era of LLMs?

• URL: http://arxiv.org/abs/2505.18215v1
• Date: Fri, 23 May 2025 05:46:42 GMT
• Title: Do BERT-Like Bidirectional Models Still Perform Better on Text Classification in the Era of LLMs?
• Authors: Junyan Zhang, Yiming Huang, Shuliang Liu, Yubo Gao, Xuming Hu,
• Abstract summary: This study challenges the prevailing "LLM-centric" trend by systematically comparing three category methods.<n>Our findings reveal that BERT-like models often outperform LLMs.<n>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.
• Score: 13.077853383476974
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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