The 2025 research introduces PPDS, an innovative dialogue system designed to solve character inconsistency in open-domain AI conversations. Researchers developed a persona extraction model based on the T5 architecture to automatically build a massive, diverse dataset from millions of social media comments. The primary dialogue engine utilizes a unified Transformer (UniLM) backbone, which efficiently processes concatenated persona and context data to generate responses. To ensure the model does not become over-reliant on specific character traits, a persona augmentation technique was implemented to introduce unrelated facts during training. Comparative tests confirm that this system significantly outperforms existing models like DialoGPT in maintaining a stable and coherent personality. Ultimately, the study provides a robust framework for creating more personable and reliable industrial chatbots. Source: 2025. Dialogue Language Model with Large-Scale Persona Data Engineering. Hong Kong Polytechnic University, AI Group, WeBank Co., Ltd. Mengze Hong, Chen Jason Zhang, Chaotao Chen, Rongzhong Lian, Di Jiang. https://aclanthology.org/2025.naacl-industry.71.pdf.