Few Tokens, Big Leverage: Preserving Safety Alignment by Constraining Safety Tokens during Fine-tuning
arXiv:2603.07445v2 Announce Type: replace-cross Abstract: Large language models (LLMs) often require fine-tuning (FT) to perform well on downstream tasks, but FT can induce safety-alignment drift even when the training dataset contains only benign data. Prior work shows that introducing a small fraction of harmful data can substantially compromise LLM refusal behavior, causing LLMs to comply with harmful requests. Existing defense methods often rely on model-wide interventions, such as restricting which parameters are updated or injecting additional safety data, which can limit generality and
The proliferation of fine-tuned language models is highlighting the critical challenge of maintaining safety alignment without sacrificing performance, prompting focused research into effective and efficient mitigation strategies.
Ensuring the safety and ethics of advanced AI models is paramount for their responsible deployment and public trust, directly impacting their societal integration and regulatory landscape.
This research suggests a more targeted and potentially efficient method for preserving safety alignment during fine-tuning, moving beyond model-wide interventions that can limit generality.
- · AI developers
- · Organizations deploying fine-tuned LLMs
- · AI safety researchers
- · Regulators
- · Malicious actors attempting to exploit LLMs
- · Current inefficient safety alignment methods
Further research and adoption of 'safety token' constrained fine-tuning methods for LLMs.
Reduced incidence of safety-alignment drift in deployed AI systems, leading to increased trust and broader application.
The development of industry standards and best practices around constrained fine-tuning for safety, potentially influencing future AI development guidelines.
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Read at arXiv cs.LG