Not All Personas Are Worth It: Culture-Reflective Persona Data Augmentation
This work addresses the problem of building culturally aware AI systems for conversational applications, though it is incremental as it extends existing persona-based methods to a specific cultural context.
The authors tackled the lack of cultural diversity in persona datasets for conversational AI by proposing a two-step pipeline to generate culture-specific personas and introducing KoPersona, a dataset of 200,000 personas capturing Korean cultural values, with evaluation showing its quality and relevance.
Incorporating personas into conversational AI models is crucial for achieving authentic and engaging interactions. However, the cultural diversity and adaptability of existing persona datasets is often overlooked, reducing their efficacy in building culturally aware AI systems. To address this issue, we propose a two-step pipeline for generating culture-specific personas and introduce KoPersona, a dataset comprising 200,000 personas designed to capture Korean cultural values, behaviors, and social nuances. A comprehensive evaluation through various metrics validates the quality of KoPersona and its relevance to Korean culture. This work not only contributes to persona-based research, but also establishes a scalable approach for creating culturally relevant personas adaptable to various languages and cultural contexts.