Papers with scalable and robust solution
Defensive Prompt Patch: A Robust and Generalizable Defense of Large Language Models against Jailbreak Attacks (2025.findings-acl)
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| Challenge: | Recent advances in large language models (LLMs) have showcased their ability to understand and generate text akin to human interaction. |
| Approach: | They propose a prompt-based defense mechanism specifically designed to protect LLMs against jailbreak attacks by introducing jailbreak prompts into malicious queries. |
| Outcome: | Empirical results show that the proposed defense outperforms existing defense strategies in balancing safety and utility while maintaining high utility. |
Rethinking Prompt Optimizers: From Prompt Merits to Optimization (2026.eacl-long)
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Zixiao Zhu, Hanzhang Zhou, Zijian Feng, Tianjiao Li, Chua Jia Jim Deryl, Lee Onn Mak, Gee Wah Ng, Kezhi Mao
| Challenge: | Existing methods to optimize prompts rely on LLMs' self-generation ability but lack interpretability due to implicit optimization. |
| Approach: | They propose a model-agnostic prompt quality merits and a merit-guided, locally deployable prompt optimizer trained on a lightweight LLM to improve prompt quality. |
| Outcome: | The proposed model avoids online optimization, reduces privacy concerns, and generalizes effectively to both large-scale and lightweight inference models. |