DataShield: Safety-degrading Data Filtering for LLM Benign Instruction Fine-Tuning
Large language models (LLMs) suffer from degraded safety capabilities even when fine-tuned with benign datasets. However, existing methods for identifying safety-degrading samples in benign datasets suffer from high computational costs and significant noise issues. In this paper, we propose DataShield to efficiently and effectively identify potential safety-degrading samples. Our key intuition is based on the observation that benign fine-tuning increases the overall response compliance of LLMs. DataShield's key technical insight is to quantify each sample's contribution to the model's compliance behavior as its safety degradation score. DataShield consists of three core components: (1) Co...
| Authors | Junbo Zhang, Qianli Zhou, Xinyang Deng et al. |
|---|---|
| Primary category | cs.CR |
| Published | 29 May 2026 |
| Last updated at source | 29 May 2026 |
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