Papers by Fenghua Weng
DELMAN: Dynamic Defense Against Large Language Model Jailbreaking with Model Editing (2025.findings-acl)
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| Challenge: | Existing safety mechanisms for Large Language Models (LLMs) are inadequate to protect against jailbreak attacks, resulting in performance degradation on general tasks. |
| Approach: | They propose a method that directly updates a minimal set of relevant parameters to neutralize harmful behaviors while preserving the model’s utility. |
| Outcome: | The proposed model outperforms baseline methods in mitigating jailbreak attacks while preserving the model’s utility. |
Adversary-Aware DPO: Enhancing Safety Alignment in Vision Language Models via Adversarial Training (2025.findings-emnlp)
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| Challenge: | Adversary-aware DPO (ADPO) is a training framework that explicitly considers adversary. |
| Approach: | a new framework integrates adversarial training into a pre-trained large language model to enhance safety alignment . adversary-aware DPO provides a framework that explicitly considers adversary . |
| Outcome: | a new training framework outperforms baselines in safety alignment and general utility of large language models. |