Robust Fine‐Tuning Based on Fuzz Testing for Chinese BERT‐Based PLMs

Summary

RoFuBERT is a robust fine-tuning framework for Chinese BERT-based pre-trained language models that uses fuzz testing. It extracts basis, glyph, pinyin and semantic features during fine-tuning, feeds them into fuzz testing to assess completeness under adversarial attacks, then retrains the model. Compared with two baselines, it takes 0.69 times the time and reduces attack success rate by 37.76%, with adversarial sample modification rate improved by 5.19% on average.

Mitigation

RoFuBERT itself, a robust fine-tuning framework using fuzz testing with Chinese basis, glyph, pinyin and semantic features, retraining the model after fuzz-based evaluation.

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