arXiv:2606.03576v1 Announce Type: new Abstract: Scholarly text classification supports literature organization, subject indexing, and research intelligence, but Chinese scholarly corpora often contain imbalanced and semantically adjacent disciplinary labels. We propose AutoTail-BSFGM, a class-balance-aware fine-tuning method that combines an automatically gated tail-prior adjustment, a weak Balanced Softmax auxiliary loss, and Fast Gradient Method adversarial regularization. The method changes only the training objective and procedure; inference uses the same single base-size encoder and linea
The proliferation of AI models for specific language tasks necessitates continuous improvement in handling domain-specific classification challenges, particularly for languages like Chinese with complex linguistic structures and data imbalances.
Improving scholarly text classification for Chinese texts can enhance information retrieval, research intelligence, and data organization, benefiting academic institutions and potentially defense or intelligence sectors with significant Chinese language data.
This research provides a more robust fine-tuning method for AI models working with imbalanced Chinese scholarly datasets, leading to more accurate classification and potentially better insights from vast Chinese scientific literature.
- · Chinese AI research community
- · Academic institutions (China)
- · Text classification software providers
- · Libraries and information scientists
- · Outdated text classification methods
- · Research reliant on less accurate Chinese text analysis
Improved accuracy in categorizing Chinese scholarly articles aids in better knowledge discovery and trend identification.
Enhanced analysis of Chinese scientific output could accelerate specific research areas by making relevant literature more discoverable.
More efficient processing and understanding of Chinese academic and technical information could subtly influence global research competitiveness and potentially national intelligence capabilities.
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Read at arXiv cs.CL