  This paper presents a simple and cost-effective method for synthesizing data
to train question-answering systems. For training, fine-tuning GPT models is a
common practice in resource-rich languages like English, however, it becomes
challenging for non-English languages due to the scarcity of sufficient
question-answer (QA) pairs. Existing approaches use question and answer
generators trained on human-authored QA pairs, which involves substantial human
expenses. In contrast, we use an instruct-tuned model to generate QA pairs in a
zero-shot or few-shot manner. We conduct experiments to compare various
strategies for obtaining QA pairs from the instruct-tuned model. The results
demonstrate that a model trained on our proposed synthetic data achieves
comparable performance to a model trained on manually curated datasets, without
incurring human costs.
