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.

MANILA – Job opportunities in Japan's health industry continue to attract Filipinos a decade since it started accepting candidate nurses and caregivers under a bilateral economic agreement. Earlier this month, a new group of Filipino health workers who aspire to work as nurses and caregivers here began preparatory training in the Japanese language and culture in two centers in Manila. The 341 applicants comprise the 12th batch of candidate nurses and caregivers under the Japan-Philippines Economic Partnership Agreement forged in 2008. Japan accepted the first batch of Filipino health workers in 2009. And I think I will broaden my experience and learn more there.