Papers by Chongwen Zhao
Unraveling LLM Jailbreaks Through Safety Knowledge Neurons (2026.eacl-long)
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| Challenge: | Large Language Models (LLMs) have achieved significant progress in alignment, ensuring safer and more reliable outputs. |
| Approach: | They propose a neuron-level interpretability method that focuses on the role of safety-related knowledge neurons to improve model robustness against jailbreak attacks. |
| Outcome: | The proposed method reduces attack success rates across multiple LLMs and outperforms all baseline defenses. |