arXiv:2606.19168v1 Announce Type: new Abstract: To achieve deeper safety alignment for large language models (LLMs), recent efforts have studied how to push safety interventions earlier into the pretraining stage, primarily by filtering unsafe data or rewriting it into safer forms. We argue that pretraining-stage alignment should go beyond making the data safe: LLMs may compose seemingly benign knowledge and capabilities into unsafe behaviors. To this end, we propose Safety Reflection Pretraining, a pretraining-stage alignment method which regularly inserts short safety reflections into pretra
The increasing sophistication and potential for misuse of large language models necessitates earlier and more robust safety interventions during their development.
Ensuring the safety and ethical alignment of AI models at the pretraining stage is crucial for their responsible deployment and to prevent the emergence of harmful autonomous behaviors.
Pretraining alignment shifts from merely filtering 'unsafe' data to proactively integrating 'safety reflections,' potentially leading to more intrinsically safe LLMs.
- · AI developers focused on ethical AI
- · End-users of AI applications
- · AI safety researchers
- · Regulatory bodies (potentially)
- · Malicious actors attempting to misuse LLMs
- · Developers solely focused on performance without safety
This method aims to produce large language models that are more intrinsically safe from their foundational training.
Safer LLMs could accelerate their adoption in sensitive applications and reduce the burden of post-deployment safety monitoring.
The widespread integration of such techniques might contribute to a global standard for ethical AI development, potentially influencing future AI policy and regulation.
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Read at arXiv cs.AI