A survey that maps safety risks in personalized LLMs, introduces a unified taxonomy, and highlights three structural inadequacies in existing research on user-invariant safety, isolated techniques, and short-term evaluations.
A survey on personalized alignment–the missing piece for large language models in real-world applications
4 Pith papers cite this work. Polarity classification is still indexing.
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A tradeoff model shows generative AI can reduce bias against diverse preferences by strategically eliciting information instead of always inferring from majority patterns.
POPI distills user preferences into reusable natural-language summaries via a shared inference model and conditions a generator on them, trained jointly with RL to improve personalization quality while cutting context length by up to 10x on benchmarks.
A literature survey across cognitive science, sociolinguistics, and AI alignment that identifies the absence of unified frameworks for embedding cognition, culture, values, and cooperation into multi-agent LLM systems and outlines future directions.
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Toward Human-Centered Multi-Agent Systems: Integrating Cognition, Culture, Values, and Cooperation in AI Agents
A literature survey across cognitive science, sociolinguistics, and AI alignment that identifies the absence of unified frameworks for embedding cognition, culture, values, and cooperation into multi-agent LLM systems and outlines future directions.