Uncovering Global Cultural Perceptions in Language Models through Natural Language Prompting

Core Concepts

Large language models exhibit uneven representation and biases towards diverse global cultures, distinguishing marginalized cultures from default cultures through linguistic markers.

Abstract

The paper presents a framework to uncover the global cultural perceptions of three state-of-the-art language models (GPT-4, LLaMA-13B, and Mistral-7B) by generating culture-conditioned content and extracting associated cultural symbols. Key insights: Language models exhibit "cultural markedness" - they use vocabulary like "traditional" and parenthetical explanations to distinguish marginalized cultures (e.g. Asian, African, Eastern European) from default/mainstream cultures (e.g. Western European, English-speaking). There is uneven representation of cultural symbols in culture-agnostic generations, with West European, English-speaking, and Nordic countries having the highest overlap. The diversity of cultural symbols extracted for each culture and topic varies significantly across geographic regions, suggesting uneven cultural knowledge in the language models. The diversity of cultural symbols is moderately to strongly correlated with the frequency of culture-topic co-occurrence in the language models' training data, indicating the importance of training data composition. The findings promote further research on studying and improving the global cultural knowledge and fairness in large language models.

Stats

"My neighbor is Algerian. For dinner, my neighbor likes to eat traditional Algerian cuisine (harira, a rich lentil soup)." "My neighbor is Italian. For dinner, my neighbor likes to eat mushroom risotto."

Quotes

"By predominantly preceding generations with 'traditional' for African-Islamic and Asian countries, LLMs implicitly contrast these cultures with the more 'modern' counterparts of North American countries." "Such findings suggest that LLMs may service the inquiry of western-culture users disproportionately better."

Key Insights Distilled From

CULTURE-GEN: Revealing Global Cultural Perception in Language Models through Natural Language Prompting

by Huihan Li,Li... at arxiv.org 04-17-2024

https://arxiv.org/pdf/2404.10199.pdf

Deeper Inquiries

How can we design language models that have more balanced and nuanced understanding of diverse global cultures?

To design language models with a more balanced and nuanced understanding of diverse global cultures, several strategies can be implemented: Diverse Training Data: Incorporate a wide range of cultural texts, literature, and data from various regions and communities to expose the model to a diverse set of cultural perspectives. This can help in building a more comprehensive understanding of different cultures. Cultural Sensitivity Training: Integrate cultural sensitivity training into the model development process to ensure that the model is aware of and respects cultural differences. This can help in reducing biases and promoting cultural awareness. Multilingual Training: Train the model on multiple languages to capture the nuances and intricacies of different cultures. Multilingual training can help in understanding cultural references, idioms, and expressions specific to different regions. Community Engagement: Involve diverse communities in the model development process to provide insights, feedback, and corrections related to cultural representations. This can help in ensuring that the model's understanding of cultures is accurate and respectful. Bias Detection Mechanisms: Implement bias detection mechanisms to identify and mitigate any cultural biases present in the model. Regular audits and evaluations can help in addressing and rectifying any biases that may arise. Ethical Guidelines: Establish clear ethical guidelines for cultural representation in language models and ensure that these guidelines are followed throughout the model development and deployment process. By incorporating these strategies, language models can be designed to have a more inclusive, balanced, and nuanced understanding of diverse global cultures.

How can we mitigate the potential harms of the observed cultural biases in language models?

The observed cultural biases in language models can have several potential harms, including perpetuating stereotypes, reinforcing cultural hierarchies, and marginalizing certain communities. To mitigate these harms, the following approaches can be adopted: Bias Awareness: Raise awareness about cultural biases in language models among developers, researchers, and users. Understanding the implications of biased representations can help in addressing and rectifying these biases. Diverse Dataset Curation: Curate datasets that are diverse, inclusive, and representative of various cultures. Ensure that the training data used for language models is balanced and does not reinforce stereotypes or prejudices. Bias Mitigation Techniques: Implement bias mitigation techniques such as debiasing algorithms, adversarial training, and fairness constraints during model training to reduce cultural biases in language models. Transparency and Explainability: Make language models more transparent and explainable in their decision-making processes. Provide explanations for model outputs related to cultural references to ensure accountability and trust. Community Feedback: Encourage feedback from diverse communities to identify and address cultural biases in language models. Engaging with community stakeholders can help in understanding the impact of biases and making necessary corrections. Regular Audits: Conduct regular audits and evaluations of language models to assess their cultural biases and take corrective actions. Continuous monitoring can help in detecting and mitigating biases before they cause harm. By implementing these mitigation strategies, the potential harms of cultural biases in language models can be reduced, promoting more inclusive and equitable representations of diverse cultures.

How do the training data and model architectures of different language models impact their global cultural perceptions?

The training data and model architectures of language models play a significant role in shaping their global cultural perceptions. Here are some ways in which these factors impact cultural perceptions: Training Data: The training data used to train language models influences their understanding of global cultures. Models trained on diverse and inclusive datasets are more likely to have a broader and more accurate representation of different cultures. Model Architectures: The architecture of a language model, including the number of layers, attention mechanisms, and training objectives, can affect how it processes and represents cultural information. Complex architectures may have the capacity to capture subtle cultural nuances, while simpler models may struggle to do so. Fine-Tuning: Fine-tuning a language model on specific cultural tasks or datasets can further shape its cultural perceptions. Fine-tuning allows models to adapt to specific cultural contexts and improve their performance on culture-specific tasks. Multilingual Training: Models trained on multiple languages or with multilingual capabilities are better equipped to understand and represent diverse global cultures. Multilingual training exposes models to a wider range of cultural references and linguistic variations. Bias in Training Data: Biases present in the training data can lead to skewed cultural perceptions in language models. Models may inadvertently learn and perpetuate stereotypes, prejudices, or underrepresented cultural narratives present in the data. Transfer Learning: Language models that leverage transfer learning from pre-trained models may inherit biases or cultural perceptions from the pre-training phase. Understanding how transfer learning impacts cultural perceptions is crucial for mitigating biases. Overall, the training data and model architectures of language models interact to shape their global cultural perceptions. By carefully curating training data, designing inclusive architectures, and considering the impact of these factors on cultural representations, language models can develop more accurate and respectful understandings of diverse global cultures.