  Large language models (LLMs) have rapidly evolved as the foundation of
various natural language processing (NLP) applications. Despite their wide use
cases, their understanding of culturally-related concepts and reasoning remains
limited. Meantime, there is a significant need to enhance these models'
cultural reasoning capabilities, especially concerning underrepresented
regions. This paper introduces a novel pipeline for extracting high-quality,
culturally-related instruction tuning datasets from vast unstructured corpora.
We utilize a self-instruction generation pipeline to identify cultural concepts
and trigger instruction. By integrating with a general-purpose instruction
tuning dataset, our model demonstrates enhanced capabilities in recognizing and
understanding regional cultural nuances, thereby enhancing its reasoning
capabilities. We conduct experiments across three regions: Singapore, the
Philippines, and the United States, achieving performance improvement of up to
6%. Our research opens new avenues for extracting cultural instruction tuning
sets directly from unstructured data, setting a precedent for future
innovations in the field.
