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 Harmfulnes
The rapid deployment and finetuning of Large Language Models (LLMs) by a wide range of actors necessitate urgent solutions to prevent misuse and ensure safety, as malicious or even seemingly benign data can quickly compromise model alignment.
Ensuring the safety and alignment of LLMs during finetuning is critical for their responsible deployment, preventing the propagation of harmful biases, and maintaining public trust in AI systems.
The introduction of principled filtering methods like GradShield allows for more secure and reliable LLM finetuning, potentially reducing the risk of 'jailbreaking' or implicit steering towards misaligned behaviors.
- · AI developers
- · Enterprises deploying LLMs
- · AI safety researchers
- · Users of AI applications
- · Malicious actors
- · Propagators of harmful data
Increased trustworthiness and safety of finetuned LLMs for various applications.
Accelerated adoption of LLMs in sensitive domains due to enhanced safety guarantees.
The emergence of new regulatory frameworks or industry standards for AI model finetuning safety based on such technical capabilities.
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