Dialogue Language Model with Large-Scale Persona Data Engineering (2025)

Abstract: Maintaining persona consistency is paramount in the application of open-domain dialogue systems, as exemplified by models like ChatGPT. Despite significant advancements, the limited scale and diversity of current persona dialogue datasets remain challenges to achieving robust persona-consistent dialogue models. In this study, drawing inspiration from the success of large-scale pre-training, we introduce PPDS, an open-domain persona dialogue system that employs extensive generative pre-training on a persona dialogue dataset to enhance persona consistency. Specifically, we present a persona extraction model designed to autonomously and precisely generate vast persona dialogue datasets. Additionally, we unveil a pioneering persona augmentation technique to address the invalid persona bias inherent in the constructed dataset. Both quantitative and human evaluations consistently highlight the superior response quality and persona consistency of our proposed model, underscoring its effectiveness.

## introduction the open-domain dialogue systems have gained significant interest due to their wide industrial applications, such as customer service support @xcite , virtual assistance @xcite , and social chatbots @xcite . inspired by the recent success of large-scale pre-training in natural language processing, many neural dialogue models resort to pre-training on large-scale dialogue datasets @xcite and demonstrate substantial progress in open-domain dialogue. notable examples include dialogpt @xcite , space @xcite , and blender @xcite for english dialogue, as well as cdial-gpt @xcite , plato-2 @xcite , and eva @xcite for chinese. while these methods can generate fluent and coherent responses, maintaining context consistency -particularly persona consistency -remains a common challenge that can lead to negative experiences in real-world human-bot interactions @xcite . as illustrated in figure 1 , the well-trained dialogpt model struggles with maintaining persona consistency, i.e., revealing contradicted marriage status and generating out-of-character responses. similar issues appear more frequently with the emerging llm-based chatbots such as chatgpt and claude, where although the chatbot can comprehend user intention correctly @xcite , the response behavior often deviates from the instruction prompt and results in user dissatisfaction @xcite @xcite . to enhance user experience and preserve persona consistency, many research efforts propose introducing explicit personas into the dialogue model @xcite @xcite . for example, the persona-chat dataset @xcite represents persona through personality sentences. while such crowd-sourced datasets capture a variety of persona features, their small scale, which is limited by the high cost of annotation, prevents them from fully unlocking the potential of largescale neural dialogue models. on the other hand, the personality assignment dataset @xcite leverages persona attributes from users' social media profiles to automatically create a significantly larger persona dataset. however, the persona diversity is still limited by the attribute set of user profiles. some studies impose persona consistency using natural language inference (nli), but their effectiveness is still limited by domain mismatches with general nli datasets or the scale of dialoguespecific nli datasets @xcite . in this paper, we present an efficient solution for constructing large-scale and diverse persona dialogue data, based on which we further pre-train an open-domain persona dialogue model called ppds (pre-trained persona dialogue system) to achieve persona consistency. to construct the dataset, we propose a persona extraction model based on the existing dialogue nli dataset @xcite , using a summarization approach to automatically and accurately extract personas from large-scale dialogue datasets, such as reddit comments @xcite . strict filtering rules have been implemented to ensure the quality of the persona dataset. next, we train a large-scale transformerbased model on the persona dialogue dataset, aiming to enhance its persona consistency through extensive pre-training. finally, we conduct extensive quantitative and human evaluations to verify the superiority of our model. the contributions of this paper are summarized as follows: ## conclusion in this work, we introduce a summarization-based persona extraction model to construct a large-scale persona dialogue dataset. based on the dataset, we propose ppds, an open-domain persona dialogue system that leverages large-scale pre-training for achieving persona consistency in dialogue generation. both quantitative and qualitative evaluations demonstrate the effectiveness of our approach. given that the experiments were conducted with relatively cost-efficient models and still yielded promising results, this work encourages future research to apply these techniques in building largescale dialogue models and enhancing dialogue generation systems for industrial applications. beyond the discussed techniques, we also encourage exploration towards the following directions in constructing better persona datasets and training persona-consistent dialogue models for different application scenarios. first, the recent emergence of llm-in-the-loop methodologies @xcite offers a promising path by incorporating the natural language understanding capabilities of llms to enhance the persona extraction process for complex, compositional personas. second, the source of extraction can extend from textual data to multimodal datasets, particularly conversational speech that contains rich persona information @xcite . lastly, extending the persona from individual behaviors to larger entities, such as brand personality @xcite , would further enhance the practical value of the proposed methods in various downstream domains, such as the hospitality and service sectors @xcite .