Papers by Bao-di Liu
Disentangled Information Bottleneck for Adversarial Text Defense (2025.emnlp-main)
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| Challenge: | Existing studies have proven that these deep models are super vulnerable to adversarial examples, which are slightly modified inputs. |
| Approach: | They propose a novel text defense method that separates the robust and non-robust features with a disentangled two-line framework rather than the one-line compression network in IB. |
| Outcome: | The proposed method outperforms six baselines on four datasets with accuracy improvements ranging from 3.8% to 20.7%. |
Enhancing the Transferability of Jailbreak Attacks on Large Language Models via Exploiting Reparameterization Invariance (2026.acl-long)
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| Challenge: | Existing token-level attacks have shown efficacy on open-source models but suffer from poor cross-model transferability. |
| Approach: | They propose a framework to improve cross-model transferability by modifying model parameters and generating update directions according to differences in output distributions rather than parameter-space distances. |
| Outcome: | The proposed framework improves cross-model transferability and success rates on open-source models. |
Adaptive Immune-based Sound-Shape Code Substitution for Adversarial Chinese Text Attacks (2024.emnlp-main)
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| Challenge: | Existing text attack methods are designed for English text, but robust implementation of Chinese text is understudied. |
| Approach: | They propose an adaptive immune-based sound-shape code algorithm for Chinese text attacks . they leverage the Sound-Shape Code to generate natural substitutions . |
| Outcome: | The proposed algorithm produces high-quality Chinese adversarial examples . it can reduce duplication of population and improve search ability . |