arXiv:2606.03648v1 Announce Type: new Abstract: Adapting foundation large language models to a user's task or preferred style through fine-tuning can result in compromising the model's safety. Previous works examined the effects of fine-tuning on model safety in limited and seemingly random experimental settings. We argue that anchoring fine-tuning to a specific capability goal is essential for avoiding arbitrary empirical choices, allowing us to draw meaningful conclusions about safety impacts, and to compare mitigation methods on a consistent basis. We conduct a multi-dimensional evaluation
As fine-tuning LLMs becomes a pervasive practice for customization, the inherent safety risks are gaining critical attention from researchers to prevent widespread negative consequences.
Ensuring the safety of fine-tuned LLMs is crucial for their responsible deployment across various industries and for maintaining public trust in AI technologies.
The focus on grounding safety measurements in capability establishes a more rigorous and comparable framework for evaluating and mitigating risks in customized AI models.
- · AI safety researchers
- · Enterprises deploying fine-tuned LLMs
- · Regulatory bodies developing AI guidelines
- · Developers neglecting safety in fine-tuning
- · Arbitrary safety evaluation methodologies
Improved safety protocols for fine-tuning LLMs will emerge more rapidly.
Standardized safety benchmarks could become a prerequisite for commercial LLM deployment.
Public confidence in personalized AI applications may increase, accelerating adoption in sensitive domains.
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Read at arXiv cs.CL