<ul><li>The paper introduces a personalized framework for LLMs. </li><li>It utilizes user-specific rewards from minimal feedback. </li><li>The method achieves significant personalization over default responses. </li><li>It leverages Reinforcement Learning from Human Feedback (RLHF). </li><li>The approach models preferences as linear combinations of base features. </li><li>Experiments validate effectiveness with synthetic and real user data. </li></ul>

The paper introduces a personalized framework for LLMs. It utilizes user-specific rewards from minimal feedback. The method achieves significant personalization over default responses. It leverages Reinforcement Learning from Human Feedback (RLHF). The approach models preferences as linear combinations of base features. Experiments validate effectiveness with synthetic and real user data.

<ul><li>The paper introduces a personalized framework for LLMs.&nbsp;</li><li>It utilizes user-specific rewards from minimal feedback.&nbsp;</li><li>The method achieves significant personalization over default responses.&nbsp;</li><li>It leverages Reinforcement Learning from Human Feedback (RLHF).&nbsp;</li><li>The approach models preferences as linear combinations of base features.&nbsp;</li><li>Experiments validate effectiveness with synthetic and real user data.&nbsp;</li></ul>

Language Model Personalization via Reward Factorization

Men know other men best. Women know other women best. 
And yes, perhaps AIs know other AIs best. 
AI explains what you should know about this week's AI research progress.

Technology

Men know other men best. Women know other women best. And yes, perhaps AIs know other AIs best. AI explains what you should know about this week's AI research progress.

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Language Model Personalization via Reward Factorization

Language Model Personalization via Reward Factorization