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cs.LG2024
Towards Aligned Data Removal via Twin Machine Unlearning
Haoxuan Ji, Zheng Lin, Yuyao Sun +4
Modern privacy regulations have spurred the evolution of machine unlearning, a technique that enables the removal of data from an already trained ML model without requiring retrain…
cs.CV2024
Interpreting and Mitigating Hallucination in MLLMs through Multi-agent Debate
Zheng Lin, Zhenxing Niu, Zhibin Wang +1
MLLMs often generate outputs that are inconsistent with the visual content, a challenge known as hallucination. Previous methods focus on determining whether a generated output is…
cs.AI2024★ 1 cited
Efficient LLM-Jailbreaking via Multimodal-LLM Jailbreak
Haoxuan Ji, Zheng Lin, Zhenxing Niu +2
This paper focuses on jailbreaking attacks against large language models (LLMs), eliciting them to generate objectionable content in response to harmful user queries. Unlike previo…