aiqbench analysis
GradShield introduces a principled filtering method that computes an Implicit Harmfulness Score to remove potentially corrupting data points before they degrade model alignment during fine-tuning. By employing adaptive thresholding, the approach successfully maintains an Attack Success Rate below 6% while preserving utility performance across various tasks. This technique offers a practical solution for practitioners concerned with mitigating both explicit and implicit safety risks in large language models.
GradShield: Alignment Preserving Finetuning
arXiv:2605.14194v2 Announce Type: replace Abstract: Large Language Models (LLMs) pose a significant risk of safety misalignment after finetuning, as models can be compromised by both explicitly and implicitly harmful data. Even some seemingly benign data can inadvertently steer a model towards misaligned behaviors. To address this, we introduce GradShield, a principled filtering method that safeguards LLMs during finetuning by identifying and removing harmful data points before they corrupt the model's alignment. It removes potentially harmful data by computing a Finetuning Implicit Harmfulness Score (FIHS) for each data point and employs an adaptive thresholding algorithm. We apply GradShield to multiple utility fine-tuning tasks across varying levels of harmful data and evaluate the safety and utility performance of the resulting LLMs using various metrics. The results show that GradShield outperforms all baseline methods, consistently maintaining an Attack Success Rate (ASR) below $6\%$ while preserving utility performance.
Abstract:Large Language Models (LLMs) pose a significant risk of safety misalignment after finetuning, as models can be compromised by both explicitly and implicitly harmful data. Even some seemingly benign data can inadvertently steer a model towards misaligned behaviors. To address this, we introduce GradShield, a principled filtering method that safeguards LLMs during finetuning by identifying and removing harmful data points before they corrupt the model's alignment. It removes potentially harmful data by computing a Finetuning Implicit Harmfulness Score (FIHS) for each data point and employs an adaptive thresholding algorithm. We apply GradShield to multiple utility fine-tuning tasks across varying levels of harmful data and evaluate the safety and utility performance of the resulting LLMs using various metrics. The results show that GradShield outperforms all baseline methods, consistently maintaining an Attack Success Rate (ASR) below $6\%$ while preserving utility performance.
Computation and Language (cs.CL)
arXiv:2605.14194 [cs.CL]
(or arXiv:2605.14194v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.14194 Focus to learn more arXiv-issued DOI via DataCite
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