Papers by Zhaohan Xi
PromptFix: Few-shot Backdoor Removal via Adversarial Prompt Tuning (2024.naacl-long)
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| Challenge: | Existing studies have shown that pre-trained language models can be backdoored such that model behavior is manipulated when trigger tokens are presented. |
| Approach: | They propose a backdoor mitigation strategy for NLP models via adversarial prompt-tuning in few-shot settings that uses two extra sets of soft tokens which approximate the trigger and counteract it respectively. |
| Outcome: | The proposed method keeps model parameters intact and only utilizes two extra sets of soft tokens which approximate the trigger and counteract it respectively. |
Data to Defense: The Role of Curation in Aligning Large Language Models Against Safety Compromise (2025.emnlp-main)
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| Challenge: | Recent studies have identified a vulnerability in large language models (LLMs) during customization. |
| Approach: | They propose an adaptive data curation approach that allows any text to be curated to enhance its effectiveness in counteracting harmful samples during customization. |
| Outcome: | The proposed approach reduces compromising effects and generates 100% safe responses. |