Culturally-Nuanced Story Generation for Reasoning in Low-Resource Languages: The Case of J avanese and S undanese (2025)

Abstract: Culturally grounded commonsense reasoning is underexplored in low-resource languages due to scarce data and costly native annotation. We test whether large language models (LLMs) can generate culturally nuanced narratives for such settings. Focusing on Javanese and Sundanese, we compare three data creation strategies: (1) LLM-assisted stories prompted with cultural cues, (2) machine translation from Indonesian benchmarks, and (3) native-written stories. Human evaluation finds LLM stories match natives on cultural fidelity but lag in coherence and correctness. We fine-tune models on each dataset and evaluate on a human-authored test set for classification and generation. LLM-generated data yields higher downstream performance than machine-translated and Indonesian human-authored training data. We release a high-quality benchmark of culturally grounded commonsense stories in Javanese and Sundanese to support future work.

## introduction reasoning, the ability to draw conclusions, make inferences, and relate concepts, is a core evaluation target in recent llm work @xcite @xcite . yet widely used english benchmarks such as storycloze @xcite , winogrande @xcite , and hellaswag @xcite encode western norms. because reasoning is culturally shaped, relying on machine-translated english datasets @xcite @xcite risks erasing local context. recent datasets for medium-resource languages (e.g., indonesian @xcite and arabic @xcite ) add cultural grounding but mainly target sentence-level classification. storylevel commonsense, how people interpret events across narratives, remains underexplored in lowresource languages due to limited speaker access, high annotation costs, and scarce culturally relevant materials. we address story comprehension in two underrepresented languages, javanese and sundanese, spoken by roughly 80m and 32m people respectively @xcite . beyond sheer scale, both carry rich sociolinguistic systems: sundanese encodes politeness and hierarchy phonologically, while javanese employs elaborate speech levels @xcite . we adopt a storycloze-style setup @xcite : given a four-sentence story, models either generate a plausible fifth sentence (generation) or choose the correct continuation from two options (classification). we compare three dataset creation strategies for culturally grounded story comprehension: (1) llm-assisted generation with culturally informed prompts, (2) machine translation from indonesian benchmarks, and (3) native-authored stories. each has distinct benefits, scalability, resource reuse, and authenticity, respectively. human evaluation shows llm outputs are culturally plausible and comparable to native-written stories. to assess cultural reasoning on narratives, we use the native-authored set as a zero-shot testbed. as in figure 1 , indonesian/sea-centric models, though covering the target languages, often produce culturally inappropriate endings, showing that language support alone is insufficient. we then fine-tune on synthetic data and find that llm-generated training yields stronger results than machine-translated data for both classification and generation. our contributions are as follows: recent work has shifted toward narrative-level reasoning with longer contexts and richer event dynamics. narrativeqa @xcite covers full books and movie scripts, cosmosqa @xcite infers implicit causes and intentions, and tellmewhy (lal et al., 2021) targets causal and motivational "why" questions. yet these remain english-centric and question-answeringoriented. our work instead addresses narrative completion in low-resource languages, particularly in javanese and sundanese, providing a culturally grounded alternative to high-resource, englishdominant benchmarks. ## dataset construction as in figure 2 , we build two parallel streams: training and test. for training, the indocloze @xcite train split serves both as seeds for llm-guided generation and as sources for machine translation into javanese and sundanese. for testing, we translate the indocloze test split and human-verify it for linguistic quality and cultural relevance, and we add a fully new set of nativeauthored stories from predefined topics. each instance follows the storycloze format: a foursentence premise with one correct and one incorrect ending. indonesian is chosen as the seed language for its national status and cultural proximity to javanese and sundanese. to ensure authenticity, native speakers authored and validated stories, embedding local names, places, foods, and customs. following @xcite ; @xcite , the dataset targets everyday commonsense reasoning grounded in local culture ## conclusion we explored the potential and limitations of llmgenerated data for commonsense reasoning and story generation in javanese and sundanese, introducing the first cloze dataset for these languages with high-quality test sets. our preliminary analysis in classification and generation settings shows that gpt-4o and claude-3 opus demonstrate strong capabilities in generating plausible short stories but face challenges in fluency and cultural accuracy. despite these limitations, our findings suggest that llm-assisted data generation is a practical and effective approach for constructing datasets in lowresource languages.