Papers by Weikai Lu
SEA: Low-Resource Safety Alignment for Multimodal Large Language Models via Synthetic Embeddings (2025.acl-long)
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| Challenge: | Existing low-resource security alignment methods struggle with the security risks posed by additional modalities. |
| Approach: | They propose to use multimodal datasets to enhance safety alignment but it is costly to construct these datasets. |
| Outcome: | Experiments on image, video, and audio-based MLLMs show that the proposed method can synthesize a high-quality embedding on a single RTX3090 GPU within 24 seconds. |
SDD: Self-Degraded Defense against Malicious Fine-tuning (2025.acl-long)
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| Challenge: | Open-source Large Language Models (LLMs) employ safety alignment methods to resist harmful instructions, but malicious fine-tuning can easily bypass these safeguards. |
| Approach: | They propose a framework to prevent malicious fine-tuning of large language models on harmful data by using alignment methods that encourage them to produce irrelevant responses to harmful prompts. |
| Outcome: | The proposed framework reduces the general capability of the LLM when malicious fine-tuning fails, rendering it incapable of following harmful instructions. |