Papers by ZiXuan Chen
Preserving Language Capabilities in Vision-Language Models via Representation Regulation (2026.findings-acl)
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| Challenge: | Vision-Language Models (VLMs) provide a unified framework to process both text-only and vision-language tasks. |
| Approach: | They propose a method to reduce the distance between visual and textual representations by introducing a Representation Distribution Difference (RDD) loss. |
| Outcome: | Empirical evidence shows that finetuning VLMs on vision-language data has degraded language capabilities. |
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. |