@inproceedings{pham-etal-2024-multi,
title = "Multi-Cultural Norm Base: Frame-based Norm Discovery in Multi-Cultural Settings",
author = "Pham, Viet Thanh and
Qu, Shilin and
Moghimifar, Farhad and
Sharma, Suraj and
Li, Yuan-Fang and
Wang, Weiqing and
Haf, Reza",
editor = "Barak, Libby and
Alikhani, Malihe",
booktitle = "Proceedings of the 28th Conference on Computational Natural Language Learning",
month = nov,
year = "2024",
address = "Miami, FL, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.conll-1.3",
pages = "24--35",
abstract = "Sociocultural norms serve as guiding principles for personal conduct in social interactions within a particular society or culture. The study of norm discovery has seen significant development over the last few years, with various interesting approaches. However, it is difficult to adopt these approaches to discover norms in a new culture, as they rely either on human annotations or real-world dialogue contents. This paper presents a robust automatic norm discovery pipeline, which utilizes the cultural knowledge of GPT-3.5 Turbo (ChatGPT) along with several social factors. By using these social factors and ChatGPT, our pipeline avoids the use of human dialogues that tend to be limited to specific scenarios, as well as the use of human annotations that make it difficult and costly to enlarge the dataset. The resulting database - Multi-cultural Norm Base (MNB) - covers 6 distinct cultures, with over 150k sociocultural norm statements in total. A state-of-the-art Large Language Model (LLM), Llama 3, fine-tuned with our proposed dataset, shows remarkable results on various downstream tasks, outperforming models fine-tuned on other datasets significantly.",
}
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%0 Conference Proceedings
%T Multi-Cultural Norm Base: Frame-based Norm Discovery in Multi-Cultural Settings
%A Pham, Viet Thanh
%A Qu, Shilin
%A Moghimifar, Farhad
%A Sharma, Suraj
%A Li, Yuan-Fang
%A Wang, Weiqing
%A Haf, Reza
%Y Barak, Libby
%Y Alikhani, Malihe
%S Proceedings of the 28th Conference on Computational Natural Language Learning
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, FL, USA
%F pham-etal-2024-multi
%X Sociocultural norms serve as guiding principles for personal conduct in social interactions within a particular society or culture. The study of norm discovery has seen significant development over the last few years, with various interesting approaches. However, it is difficult to adopt these approaches to discover norms in a new culture, as they rely either on human annotations or real-world dialogue contents. This paper presents a robust automatic norm discovery pipeline, which utilizes the cultural knowledge of GPT-3.5 Turbo (ChatGPT) along with several social factors. By using these social factors and ChatGPT, our pipeline avoids the use of human dialogues that tend to be limited to specific scenarios, as well as the use of human annotations that make it difficult and costly to enlarge the dataset. The resulting database - Multi-cultural Norm Base (MNB) - covers 6 distinct cultures, with over 150k sociocultural norm statements in total. A state-of-the-art Large Language Model (LLM), Llama 3, fine-tuned with our proposed dataset, shows remarkable results on various downstream tasks, outperforming models fine-tuned on other datasets significantly.
%U https://aclanthology.org/2024.conll-1.3
%P 24-35
Markdown (Informal)
[Multi-Cultural Norm Base: Frame-based Norm Discovery in Multi-Cultural Settings](https://aclanthology.org/2024.conll-1.3) (Pham et al., CoNLL 2024)
ACL
- Viet Thanh Pham, Shilin Qu, Farhad Moghimifar, Suraj Sharma, Yuan-Fang Li, Weiqing Wang, and Reza Haf. 2024. Multi-Cultural Norm Base: Frame-based Norm Discovery in Multi-Cultural Settings. In Proceedings of the 28th Conference on Computational Natural Language Learning, pages 24–35, Miami, FL, USA. Association for Computational Linguistics.