MELT: A Style-Adaptive Multimodal Folktale Generation Framework for Underrepresented Cultures

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.