# LaMP Benchmark

The work identifies that explicit style instructions can erode the user-specific traits that personalized LLMs aim to preserve (a failure mode it calls personalization collapse), proposes modeling personalization as a distributional residual between the user's true linguistic distribution and the base model's neutral distribution under the same input, and builds PsPLUG: a lightweight plug-in that prepends a 3-token prefix (system instruction vector, user vector, input vector) to a frozen Qwen3-8B backbone, learns the user-specific residual with a style-conditioned Bradley–Terry preference objective, and tunes personalization strength at inference via a scaling coefficient; on the LaMP benchmark, PsPLUG generally outperforms non-personalized, RAG, PAG, PPlug, and OPPU baselines without styl

The work identifies that explicit style instructions can erode the user-specific traits that personalized LLMs aim to preserve (a failure mode it calls personalization collapse), proposes modeling personalization as a distributional residual between the user's true linguistic distribution and the base model's neutral distribution under the same input, and builds PsPLUG: a lightweight plug-in that prepends a 3-token prefix (system instruction vector, user vector, input vector) to a frozen Qwen3-8B backbone, learns the user-specific residual with a style-conditioned Bradley–Terry preference objective, and tunes personalization strength at inference via a scaling coefficient; on the LaMP benchmark, PsPLUG generally outperforms non-personalized, RAG, PAG, PPlug, and OPPU baselines without styl

CARD introduces a hierarchical framework for personalized text generation: it clusters users by shared stylistic patterns and trains group-specific LoRA adapters, derives lightweight user preference vectors through implicit preference learning that contrasts user-authored text with cluster-level generations, and injects personalization at inference only via low-rank logit corrections, ranking first in 10 of 12 settings across six LaMP and LongLaMP tasks and two metrics while remaining stable for low-resource users, across model scales, and in storage efficiency.

CARD introduces a hierarchical framework for personalized text generation: it clusters users by shared stylistic patterns and trains group-specific LoRA adapters, derives lightweight user preference vectors through implicit preference learning that contrasts user-authored text with cluster-level generations, and injects personalization at inference only via low-rank logit corrections, ranking first in 10 of 12 settings across six LaMP and LongLaMP tasks and two metrics while remaining stable for low-resource users, across model scales, and in storage efficiency.