Having Beer after Prayer? Measuring Cultural Bias in Large Language Models (2305.14456v4)

Abstract: As the reach of large 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

• The paper introduces CAMeL—a dataset of 628 prompts and 20,368 entities—to evaluate bias in both multilingual and Arabic LMs.
• It finds that models tend to favor Western cultural elements, leading to stereotypical story generation and skewed recognition in NER and sentiment tasks.
• It proposes a Cultural Bias Score (CBS) to quantify bias and calls for more culturally balanced training data to enhance model fairness.

Measuring Cultural Bias in LLMs

The paper "Having Beer after Prayer? Measuring Cultural Bias in LLMs" by Naous et al. investigates the cultural biases inherent in large LMs, particularly focusing on the disparity between Western and Arab cultural contexts. This paper introduces a comprehensive evaluation resource, named CAMeL, to assess these biases in multilingual and Arabic monolingual LLMs.

Study Objectives and Methodology

The primary objective of the research is to examine the extent to which LLMs exhibit bias towards Western culture when they are expected to be operating within Arabic linguistic contexts. The paper constructs CAMeL by curating 628 prompts and 20,368 entities that are representative of both Arab and Western cultures across eight distinct categories, such as person names, food dishes, beverages, and sports clubs. These resources are used for empirical evaluations, including intrinsic and extrinsic assessments that explore models' performances in tasks like story generation, named entity recognition (NER), sentiment analysis, and text infilling.

Key Findings

1. Western Bias in LMs: The paper reveals that both multilingual and Arabic monolingual models tend to demonstrate a preference for Western entities, even when presented with clearly defined Arab cultural prompts. This observation is consistent across several tasks and models.
2. Stereotypes in Story Generation: In story-generation tasks, LMs frequently exhibited stereotypes. Adjectives related to poverty and traditionalism appeared more frequently in stories about Arab-named characters, whereas terms suggesting wealth and high-status were more often associated with Western names.
3. NER and Sentiment Analysis Discrepancies: In the context of NER, models performed more accurately when recognizing Western names and locations compared to Arab ones. Sentiment analysis tasks revealed an unfounded association of Arab entities with negative sentiment, showcasing unfair biases in LLMs.
4. Cultural Bias Scores: The researchers propose a Cultural Bias Score (CBS) to quantify the inclination of LMs towards Western culture within culturally contextualized prompts. High CBS values indicate a significant Western bias, highlighting the models' struggle to adapt adequately to cultural nuances.

Implications and Future Directions

The implications of this research are multifaceted. From a practical standpoint, the existence of such biases in LMs can affect user experiences, leading to misrepresentation and misunderstanding, especially in non-Western cultural contexts. Theoretical implications include the need to re-evaluate the training corpora used for building these models, as it can heavily influence cultural biases. The paper's analysis of six Arabic corpora underscores that sources like Wikipedia, often deemed high-quality, might inadvertently foster Western-centric content.

Future research should explore strategies for mitigating these biases, possibly by leveraging more culturally relevant training data or enhancing model architectures to better handle diverse cultural contexts. Additionally, extending CAMeL to cover more languages and cultural distinctions would potentially provide deeper insights into the cross-cultural capabilities of LLMs.

Conclusion

Naous et al.’s work provides important insights into cultural biases in LLMs, underlining the necessity for culturally aware AI systems. Through CAMeL and their rigorous evaluation methodology, the authors offer a valuable tool for the community to assess and improve LMs' performance in terms of cultural sensitivity and fairness.