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            <title>Proceedings of the 2nd Workshop on Cross-Cultural Considerations in NLP</title>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T CRAFT: Extracting and Tuning Cultural Instructions from the Wild
%A Wang, Bin
%A Lin, Geyu
%A Liu, Zhengyuan
%A Wei, Chengwei
%A Chen, Nancy
%Y Prabhakaran, Vinodkumar
%Y Dev, Sunipa
%Y Benotti, Luciana
%Y Hershcovich, Daniel
%Y Cabello, Laura
%Y Cao, Yong
%Y Adebara, Ife
%Y Zhou, Li
%S Proceedings of the 2nd Workshop on Cross-Cultural Considerations in NLP
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F wang-etal-2024-craft
%X 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.
%R 10.18653/v1/2024.c3nlp-1.4
%U https://aclanthology.org/2024.c3nlp-1.4/
%U https://doi.org/10.18653/v1/2024.c3nlp-1.4
%P 42-47

Markdown (Informal)

[CRAFT: Extracting and Tuning Cultural Instructions from the Wild](https://aclanthology.org/2024.c3nlp-1.4/) (Wang et al., C3NLP 2024)

• CRAFT: Extracting and Tuning Cultural Instructions from the Wild (Wang et al., C3NLP 2024)

ACL

• Bin Wang, Geyu Lin, Zhengyuan Liu, Chengwei Wei, and Nancy Chen. 2024. CRAFT: Extracting and Tuning Cultural Instructions from the Wild. In Proceedings of the 2nd Workshop on Cross-Cultural Considerations in NLP, pages 42–47, Bangkok, Thailand. Association for Computational Linguistics.