Self-Pluralising Culture Alignment for Large Language Models

• URL: http://arxiv.org/abs/2410.12971v1
• Date: Wed, 16 Oct 2024 19:06:08 GMT
• Title: Self-Pluralising Culture Alignment for Large Language Models
• Authors: Shaoyang Xu, Yongqi Leng, Linhao Yu, Deyi Xiong,
• Abstract summary: We propose CultureSPA, a framework that allows large language models to align to pluralistic cultures. By comparing culture-aware/unaware outputs, we are able to detect and collect culture-related instances. Extensive experiments demonstrate that CultureSPA significantly improves the alignment of LLMs to diverse cultures without compromising general abilities.
• Score: 36.689491885394034
• License:
• Abstract: As large language models (LLMs) become increasingly accessible in many countries, it is essential to align them to serve pluralistic human values across cultures. However, pluralistic culture alignment in LLMs remain an open problem. In this paper, we propose CultureSPA, a Self-Pluralising Culture Alignment framework that allows LLMs to simultaneously align to pluralistic cultures. The framework first generates questions on various culture topics, then yields LLM outputs in response to these generated questions under both culture-aware and culture-unaware settings. By comparing culture-aware/unaware outputs, we are able to detect and collect culture-related instances. These instances are employed to fine-tune LLMs to serve pluralistic cultures in either a culture-joint or culture-specific way. Extensive experiments demonstrate that CultureSPA significantly improves the alignment of LLMs to diverse cultures without compromising general abilities. And further improvements can be achieved if CultureSPA is combined with advanced prompt engineering techniques. Comparisons between culture-joint and culture-specific tuning strategies, along with variations in data quality and quantity, illustrate the robustness of our method. We also explore the mechanisms underlying CultureSPA and the relations between different cultures it reflects.

Related papers

• Survey of Cultural Awareness in Language Models: Text and Beyond [39.77033652289063]
  Large-scale deployment of large language models (LLMs) in various applications requires LLMs to be culturally sensitive to the user to ensure inclusivity. Culture has been widely studied in psychology and anthropology, and there has been a recent surge in research on making LLMs more culturally inclusive.
  arXiv  Detail & Related papers  (2024-10-30T16:37:50Z)
• Self-Alignment: Improving Alignment of Cultural Values in LLMs via In-Context Learning [13.034603322224548]
  We present a simple and inexpensive method that uses a combination of in-context learning (ICL) and human survey data. We show that our method could prove useful in test languages other than English and can improve alignment to the cultural values that correspond to a range of culturally diverse countries.
  arXiv  Detail & Related papers  (2024-08-29T12:18:04Z)
• Translating Across Cultures: LLMs for Intralingual Cultural Adaptation [12.5954253354303]
  We define the task of cultural adaptation and create an evaluation framework to evaluate the performance of modern LLMs. We analyze possible issues with automatic adaptation. We hope that this paper will offer more insight into the cultural understanding of LLMs and their creativity in cross-cultural scenarios.
  arXiv  Detail & Related papers  (2024-06-20T17:06:58Z)
• CulturePark: Boosting Cross-cultural Understanding in Large Language Models [63.452948673344395]
  This paper introduces CulturePark, an LLM-powered multi-agent communication framework for cultural data collection. It generates high-quality cross-cultural dialogues encapsulating human beliefs, norms, and customs. We evaluate these models across three downstream tasks: content moderation, cultural alignment, and cultural education.
  arXiv  Detail & Related papers  (2024-05-24T01:49:02Z)
• Understanding the Capabilities and Limitations of Large Language Models for Cultural Commonsense [98.09670425244462]
  Large language models (LLMs) have demonstrated substantial commonsense understanding. This paper examines the capabilities and limitations of several state-of-the-art LLMs in the context of cultural commonsense tasks.
  arXiv  Detail & Related papers  (2024-05-07T20:28:34Z)
• CULTURE-GEN: Revealing Global Cultural Perception in Language Models through Natural Language Prompting [73.94059188347582]
  We uncover culture perceptions of three SOTA models on 110 countries and regions on 8 culture-related topics through culture-conditioned generations. We discover that culture-conditioned generation consist of linguistic "markers" that distinguish marginalized cultures apart from default cultures.
  arXiv  Detail & Related papers  (2024-04-16T00:50:43Z)
• Does Mapo Tofu Contain Coffee? Probing LLMs for Food-related Cultural Knowledge [47.57055368312541]
  We introduce FmLAMA, a multilingual dataset centered on food-related cultural facts and variations in food practices. We analyze LLMs across various architectures and configurations, evaluating their performance in both monolingual and multilingual settings.
  arXiv  Detail & Related papers  (2024-04-10T08:49:27Z)
• Not All Countries Celebrate Thanksgiving: On the Cultural Dominance in Large Language Models [89.94270049334479]
  This paper identifies a cultural dominance issue within large language models (LLMs) LLMs often provide inappropriate English-culture-related answers that are not relevant to the expected culture when users ask in non-English languages.
  arXiv  Detail & Related papers  (2023-10-19T05:38:23Z)
• Cultural Alignment in Large Language Models: An Explanatory Analysis Based on Hofstede's Cultural Dimensions [10.415002561977655]
  This research proposes a Cultural Alignment Test (Hoftede's CAT) to quantify cultural alignment using Hofstede's cultural dimension framework. We quantitatively evaluate large language models (LLMs) against the cultural dimensions of regions like the United States, China, and Arab countries. Our results quantify the cultural alignment of LLMs and reveal the difference between LLMs in explanatory cultural dimensions.
  arXiv  Detail & Related papers  (2023-08-25T14:50:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.