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This research introduces POPI, a framework that personalizes large language models by distilling individual user preferences into concise natural language summaries. By decoupling inference from generation, the method enables efficient reuse of these summaries across different model architectures, including frozen commercial APIs, while significantly reducing context overhead. The approach demonstrates that optimizing for both generation accuracy and summary informativeness within a unified reinforcement learning objective can effectively bridge population-level alignment with individual user needs.
POPI: Personalizing LLMs via Optimized Natural Language Preference Inference
arXiv:2510.17881v4 Announce Type: replace-cross Abstract: Large language models (LLMs) are typically aligned with population-level preferences, despite substantial variation across individual users. We introduce POPI, a user-level personalization framework that separates the problem into two components connected by a natural-language interface: a shared inference model that distills heterogeneous user signals into a concise preference summary, and a shared generator that conditions on this summary to produce personalized responses. Both components are trained under a unified preference-optimization objective, with reinforcement learning handling the non-differentiable inference step. This objective decomposes into generator approximation error and summary informativeness, revealing how a single loss simultaneously drives accurate generation and informative summarization. Because the interface is natural language, learned summaries can be inferred once per user and reused across different generators -- including frozen, black-box commercial APIs. Across four personalization benchmarks, POPI generally improves personalization quality while reducing context overhead by up to an order of magnitude.
Abstract:Large language models (LLMs) are typically aligned with population-level preferences, despite substantial variation across individual users. We introduce POPI, a user-level personalization framework that separates the problem into two components connected by a natural-language interface: a shared inference model that distills heterogeneous user signals into a concise preference summary, and a shared generator that conditions on this summary to produce personalized responses. Both components are trained under a unified preference-optimization objective, with reinforcement learning handling the non-differentiable inference step. This objective decomposes into generator approximation error and summary informativeness, revealing how a single loss simultaneously drives accurate generation and informative summarization. Because the interface is natural language, learned summaries can be inferred once per user and reused across different generators -- including frozen, black-box commercial APIs. Across four personalization benchmarks, POPI generally improves personalization quality while reducing context overhead by up to an order of magnitude.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2510.17881 [cs.CL]
(or arXiv:2510.17881v4 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2510.17881 Focus to learn more arXiv-issued DOI via DataCite
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