Generating culturally coherent multimodal folktales presents unique challenges, particularly for underrepresented cultures facing data scarcity and cultural bias. Existing approaches predominantly rely on Western-centric corpora, limit outputs to text–image modalities, and reduce culture to a superficial stylistic layer rather than a core narrative structure. In this paper, we propose MELT (Multimodal Engine for Localized Tales) a unified framework for culturally adaptive story generation across text, illustration, and audio. To achieve this, MELT embeds cultural context as a foundational generative constraint and integrates three modules: (1) a localized folktale generation module leveraging structured prompting and contextual reasoning to preserve the narrative flow and cultural identity of traditional folktales; (2) an illustration generation module that ensures cultural style and character consistency using a Tri-Stage LoRA (Low-Rank Adaptation) pipeline; (3) a storytelling audio component tailored to cultural prosody and oral storytelling conventions. Case studies on Korean and Bengali folktales demonstrate MELT’s ability to generate culturally coherent outputs using lightweight, data-efficient adaptation techniques. Human evaluation results demonstrated that MELT consistently outperformed the baseline across both case studies, showing a substantial improvement in cultural adaptation, nearly doubling the score in the Korean folktale evaluation. By positioning culture as a core generative principle, MELT provides a pathway for culturally coherent AI storytelling, particularly for underrepresented traditions.
Choi et al. (Wed,) studied this question.