arXiv:2606.09038v1 Announce Type: new Abstract: Large Language Models (LLMs) have enabled increasingly personalized interactions by adapting to users' preferences, contexts, and long-term histories. However, the mechanisms that enable personalization also expand the safety landscape in ways not systematically addressed by existing literature. Existing reviews typically focus either on personalization or safety, leaving their intersection largely unexplored. We present the first comprehensive, safety-aware review of personalized LLMs. We organize personalization along three dimensions-user repr
The rapid advancement and widespread adoption of Large Language Models necessitate a deeper understanding of their personalized applications and associated safety implications, which have been overlooked in prior research.
This research highlights a crucial, underexplored intersection of essential LLM capabilities, informing responsible development and deployment strategies for personalized AI systems.
The focus on the unique safety landscape created by personalized LLMs means that future development in this area will require more sophisticated risk assessments and mitigation strategies beyond generic AI safety measures.
- · AI Safety Researchers
- · LLM Developers
- · Organizations deploying personalized AI
- · Developers ignoring personalized safety risks
- · Users vulnerable to personalized exploitation
Increased awareness and demand for robust safety frameworks specific to personalized LLMs.
Development of new regulatory guidelines and industry best practices for personalized AI.
A competitive advantage for companies that can effectively balance personalization with advanced safety features in their AI offerings.
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Read at arXiv cs.AI