Papers by Jiongxiao Wang
Test-time Backdoor Mitigation for Black-Box Large Language Models with Defensive Demonstrations (2025.findings-naacl)
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| Challenge: | Existing studies on backdoor defense have focused on training phase, overlooking critical aspect of testing time defense. |
| Approach: | They propose to use demonstrations as a defense mechanism against backdoor attacks in black-box LLMs. |
| Outcome: | The proposed method outperforms existing defense baselines across most evaluation scenarios. |
Reinforcement Learning for Self-Improving Agent with Skill Library (2026.acl-long)
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Jiongxiao Wang, Qiaojing Yan, Yawei Wang, Yijun Tian, Soumya Smruti Mishra, Zhichao Xu, Megha Gandhi, Panpan Xu, Lin Lee Cheong
| Challenge: | Large Language Model (LLM)-based agents have demonstrated remarkable capabilities in complex reasoning and multi-turn interactions but struggle to continuously improve and adapt when deployed in new environments. |
| Approach: | They propose a Reinforcement Learning-based approach to enhance agents’ self-improvement capabilities with a skill library. |
| Outcome: | The proposed framework achieves 8.9% higher Scenario Goal Completion when applied to supervised-finetuned model with expert experience while requiring 26% fewer interaction steps and generating 59% fewer tokens. |
RLHFPoison: Reward Poisoning Attack for Reinforcement Learning with Human Feedback in Large Language Models (2024.acl-long)
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| Challenge: | Recent advances in Large Language Models (LLMs) have significantly enhanced the capabilities in natural language processing. |
| Approach: | They propose a method to poison large language models by using annotators to rank a set of collected responses to generate longer tokens. |
| Outcome: | The proposed method can generate longer tokens without harming the original safety alignment performance. |