As LLMs become capable of complex tasks, there is growing potential for personalized interactions tailored to the subtle and idiosyncratic preferences of the user. We present a public benchmark, PersonalLLM, focusing on adapting LLMs to provide maximal benefits for a particular user. Departing from existing alignment benchmarks that implicitly assume uniform preferences, we curate open-ended prompts paired with many high-quality answers over which users would be expected to display heterogeneous latent preferences. Instead of persona prompting LLMs based on high-level attributes (e.g., user race or response length), which yields homogeneous preferences relative to humans, we develop a method that can simulate a large user base with diverse preferences from a set of pre-trained reward models. Our dataset and generated personalities offer an innovative testbed for developing personalization algorithms that grapple with continual data sparsity---few relevant feedback from the particular user---by leveraging historical data from other (similar) users. We explore basic in-context learning and meta-learning baselines to illustrate the utility of PersonalLLM and highlight the need for future methodological development.

Experiments in online platforms frequently suffer from network interference, where treatments applied to one unit affect outcomes of connected ones, violating the Stable Unit Treatment Value Assumption (SUTVA) and substantially biasing treatment effect estimations. A common solution is to cluster connected units and randomize treatments at the cluster level, typically followed by estimation using either a simple difference-in-means (DM) estimator, which ignores remaining interference and suffers from O(δ) bias where δ measures interference strength; or the unbiased Horvitz-Thompson (HT) estimator, which eliminates bias through importance sampling but incurs exponentially high variance scaling with d, the maximum network degree. This fundamental limitation persists even with sophisticated clustering designs, creating narrow bias-variance tradeoffs often inadequate for practical applications.