Research·arXiv cs.CL·1d ago

AutoTail-BSFGM: Class-Balance-Aware Fine-Tuning for Chinese Scholarly Text Classification

Imbalanced classification remains a persistent challenge in domain-specific NLP, particularly for non-English corpora where label distributions skew heavily toward dominant categories. This work addresses Chinese scholarly text classification through a training-time intervention that combines gated tail-class reweighting, balanced softmax regularization, and adversarial robustness techniques. The approach preserves inference efficiency by keeping the base encoder and classifier unchanged, making it practical for production deployment. Results on two CSL benchmarks with 13 to 67 labels suggest meaningful gains on minority classes without sacrificing majority-class performance, a critical trade-off in real-world document organization systems.

Modelwire context

Explainer

The key innovation is that AutoTail-BSFGM keeps the base model frozen during fine-tuning, applying reweighting only at the loss level. This means practitioners can deploy the method without retraining inference pipelines, a practical constraint that prior class-balance work often ignores.

This connects directly to the clinical text categorization work from June 1st, which fine-tuned Llama-3 for discipline-level provenance tagging on imbalanced MIMIC-III data. Both papers tackle the same underlying problem: domain-specific text classification where label distributions are skewed. The AutoTail-BSFGM contribution is narrower (training-time intervention only, no foundation model involved) but addresses the deployment friction that the clinical work sidesteps by working within a single fine-tuned model. For practitioners building multi-label systems in regulated domains where redeploying inference is costly, this approach offers a middle ground between full retraining and accepting performance gaps on minority classes.

If AutoTail-BSFGM shows comparable minority-class gains on out-of-domain Chinese text classification benchmarks (e.g., applied to a new scholarly corpus not in the CSL dataset), that confirms the method generalizes beyond the two reported benchmarks. If gains collapse on held-out domains, the results are likely benchmark-specific rather than methodologically robust.

Coverage we drew on

Towards Multidisciplinary Summarization of Hospital Stays: Efficient Sentence-Level Clinical Provenance Categorization · arXiv cs.CL

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MentionsAutoTail-BSFGM · Balanced Softmax · Fast Gradient Method · CSL

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