Having Beer after Prayer? Measuring Cultural Bias in Large Language Models

• URL: http://arxiv.org/abs/2305.14456v4
• Date: Wed, 20 Mar 2024 17:16:37 GMT
• Title: Having Beer after Prayer? Measuring Cultural Bias in Large Language Models
• Authors: Tarek Naous, Michael J. Ryan, Alan Ritter, Wei Xu,
• Abstract summary: We show that multilingual and Arabic monolingual LMs exhibit bias towards entities associated with Western culture. We introduce CAMeL, a novel resource of 628 naturally-occurring prompts and 20,368 entities spanning eight types that contrast Arab and Western cultures. Using CAMeL, we examine the cross-cultural performance in Arabic of 16 different LMs on tasks such as story generation, NER, and sentiment analysis.
• Score: 25.722262209465846
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As the reach of large language models (LMs) expands globally, their ability to cater to diverse cultural contexts becomes crucial. Despite advancements in multilingual capabilities, models are not designed with appropriate cultural nuances. In this paper, we show that multilingual and Arabic monolingual LMs exhibit bias towards entities associated with Western culture. We introduce CAMeL, a novel resource of 628 naturally-occurring prompts and 20,368 entities spanning eight types that contrast Arab and Western cultures. CAMeL provides a foundation for measuring cultural biases in LMs through both extrinsic and intrinsic evaluations. Using CAMeL, we examine the cross-cultural performance in Arabic of 16 different LMs on tasks such as story generation, NER, and sentiment analysis, where we find concerning cases of stereotyping and cultural unfairness. We further test their text-infilling performance, revealing the incapability of appropriate adaptation to Arab cultural contexts. Finally, we analyze 6 Arabic pre-training corpora and find that commonly used sources such as Wikipedia may not be best suited to build culturally aware LMs, if used as they are without adjustment. We will make CAMeL publicly available at: https://github.com/tareknaous/camel

Related papers

• CamelEval: Advancing Culturally Aligned Arabic Language Models and Benchmarks [19.403924294587043]
  This paper introduces Juhaina, a Arabic-English bilingual LLM specifically designed to align with the values and preferences of Arabic speakers. Our model contains 9.24 billion parameters and is trained on a context window of up to 8,192 tokens.
  arXiv  Detail & Related papers  (2024-09-19T09:52:35Z)
• See It from My Perspective: Diagnosing the Western Cultural Bias of Large Vision-Language Models in Image Understanding [78.88461026069862]
  Vision-language models (VLMs) can respond to queries about images in many languages. We present a novel investigation that demonstrates and localizes Western bias in image understanding.
  arXiv  Detail & Related papers  (2024-06-17T15:49:51Z)
• 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)
• Investigating Cultural Alignment of Large Language Models [10.738300803676655]
  We show that Large Language Models (LLMs) genuinely encapsulate the diverse knowledge adopted by different cultures. We quantify cultural alignment by simulating sociological surveys, comparing model responses to those of actual survey participants as references. We introduce Anthropological Prompting, a novel method leveraging anthropological reasoning to enhance cultural alignment.
  arXiv  Detail & Related papers  (2024-02-20T18:47:28Z)
• 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)
• AceGPT, Localizing Large Language Models in Arabic [73.39989503874634]
  The paper proposes a comprehensive solution that includes pre-training with Arabic texts, Supervised Fine-Tuning (SFT) utilizing native Arabic instructions, and GPT-4 responses in Arabic. The goal is to cultivate culturally cognizant and value-aligned Arabic LLMs capable of accommodating the diverse, application-specific needs of Arabic-speaking communities.
  arXiv  Detail & Related papers  (2023-09-21T13:20:13Z)
• 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.