PersonaFactory: Stylistic User Prompt Augmentation with Synthetic Persona Bank

Large language models increasingly replace real people in the AI development pipeline, as they are used to generate training dialogues at scale, control alignment and evaluation. However, out-of-the-box models produce monolingual and stylistically flat texts as synthetic user prompts, failing to replicate the real user-AI communication mechanics. We present PersonaFactory, an end-to-end pipeline to generate detailed user personas, later using them to augment existing user prompts from their point of view. As part of this work, we showcase two major data contributions, facilitating research in LLM-based stylistic chat generation. First, we release a bank of nearly 100000 structured, fully synthetic personas, focusing on their background and communication behavior with a chatbot. Second, we release a corpus of roughly 236000 persona-styled synthetic conversations spanning 77 languages, produced by using the generated bank of personas to augment existing user prompts and match personas to them based on original prompt intent and profile background. We also provide an ablation study of the assistant persona design for response augmentation and downstream performance after training on such conversations. Finally, we evaluate the effect of personal features on style adoption and intent preservation, using an LLM-judge and heuristic metrics to assess augmentation efficiency. We find that simulating users' unique behavioral features in the conversation greatly benefits from a structured, attribute-based approach, compared to a single free-text description in existing personalization methods. We show that the produced augmented data is hard to distinguish from human-written texts for real people and that PersonaFactory as a training artifact can improve model adaptive behavior purely from data impact without steering.