New Fine-Tuning Method Improves Chinese Scholarly Text Classification with Imbalanced Data
Researchers introduced AutoTail-BSFGM, a fine-tuning method designed to improve classification of Chinese scholarly texts when training data has imbalanced class distributions. The method combines automated tail-class adjustment, balanced softmax loss, and adversarial regularization while maintaining the same inference architecture as baseline models. The approach shows modest but consistent improvements on abstract-to-discipline and title-to-category classification tasks, with potential applications in literature organization and research indexing.
AutoTail-BSFGM addresses a common challenge in machine learning: classifying text when some categories appear far more frequently than others in training data. The method modifies only the training process while keeping inference unchanged, using the same base encoder and classifier as standard baselines. Evaluated on two Chinese Scholarly Language (CSL) tasks—one with 67 discipline labels and another with 13 categories—the approach improved validation accuracy by 0.83 percentage points on the primary abstract task and 0.70 points on the title task when using MacBERT-base. The gains were most pronounced for balanced accuracy metrics, suggesting the method effectively addresses class imbalance. However, improvements were modest and not uniform across all metrics and data splits, indicating bounded but meaningful contribution to the problem.
What's missing
The study does not discuss computational overhead or training time comparisons with baseline methods. Additionally, generalization to non-Chinese languages or other domains beyond scholarly text classification is not addressed. The paper does not provide analysis of which types of tail classes benefit most from the proposed method.
What different sources said
- arXiv cs.AICenter
Large Language Models for Imbalanced Classification: Diversity makes the difference
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