arXiv:2509.24696v2 Announce Type: replace Abstract: Personalizing large language models (LLMs) to individual user preferences is a critical step beyond generating generically helpful responses. However, current personalization methods are ill-suited for new users, as they typically require either slow, resource-intensive fine-tuning or a substantial amount of pre-existing user data, creating a significant cold-start problem. To address this challenge, we introduce a new paradigm for real-time personalization by learning from online pairwise preference feedback collected during text generation.
The rapid deployment of large language models (LLMs) has amplified the need for personalized AI experiences, driving innovation in efficient adaptation methods.
A strategic reader should care because overcoming the cold-start problem in AI personalization unlocks new user acquisition and engagement models for LLM-powered products.
The ability to personalize LLMs in real-time with minimal data fundamentally changes how new users interact with and adopt AI tools, moving beyond generic responses.
- · AI platform providers
- · Customer service industries
- · Content recommendation engines
- · Individual users of AI
- · Generic, unpersonalized AI services
- · Legacy personalization methods requiring large datasets
- · AI models with slow adaptation cycles
Improved user satisfaction and retention for AI applications due to tailored interactions.
Accelerated expansion of AI into new consumer and enterprise segments where rapid personalization is key.
Increased data privacy concerns as more online preference feedback is collected and utilized for personalization.
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Read at arXiv cs.LG