EchoAlign: Learning Personalized Style From Parts to Whole

Users differ in how they prefer responses to be expressed, including their desired levels of style attributes such as formality and concision. Supporting diverse style preferences in production calls for adaptation from limited preference data and compact personalization parameters alongside a shared model. We introduce EchoAlign, a family of two-stage preference-learning methods that progressively encode personalized style guidance into a compact soft prompt while keeping the language-model backbone frozen. The first stage uses attribute-level preference pairs to train dedicated prompt segments, giving each segment a focused learning objective. The second stage uses overall-style preference pairs to adapt their combination so that they jointly guide generation according to the user's complete style preference. Each complete style preference has a separately learned prompt whose 256 token embeddings can be precomputed for reuse across questions, supporting pluggable personalization with shared model weights. Using approximately 100 preference pairs per profile, EchoAlign achieves relative improvements in style alignment of 55.8–62.5% over unadapted base models and 2.9–11.3% over the strongest tested LoRA baselines across three backbones. We further apply EchoAlign to personalized message generation in production, where a randomized A/B test shows a 6.49% relative increase in click-through rate. This real-user behavioral feedback suggests improved satisfaction with the generated messages.