CRAFT: Extracting and Tuning Cultural Instructions from the Wild
This work addresses the need to enhance cultural reasoning capabilities in LLMs, particularly for underrepresented regions, but it is incremental as it builds on existing instruction tuning methods.
The paper tackles the problem of limited cultural reasoning in large language models by introducing a pipeline to extract cultural instruction tuning datasets from unstructured corpora, achieving up to 6% performance improvement in experiments across Singapore, the Philippines, and the United States.
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.