Synthetic Data Generation for Culturally Nuanced Commonsense Reasoning in Low-Resource Languages

Quantifying reasoning capability in low-resource languages remains a challenge in NLP due to data scarcity and limited access to annotators. While LLM-assisted dataset construction has proven useful for medium- and high-resource languages, its effectiveness in low-resource languages, particularly for commonsense reasoning, is still unclear. In this paper, we compare three dataset creation strategies: (1) LLM-assisted dataset generation, (2) machine translation, and (3) human-written data by native speakers, to build a culturally nuanced story comprehension dataset. We focus on Javanese and Sundanese, two major local languages in Indonesia, and evaluate the effectiveness of open-weight and closed-weight LLMs in assisting dataset creation through extensive manual validation. To assess the utility of synthetic data, we fine-tune language models on classification and generation tasks using this data and evaluate performance on a human-written test set. Our findings indicate that LLM-assisted data creation outperforms machine translation.