Entangled in Representations: Mechanistic Investigation of Cultural Biases in Large Language Models

TL;DR

This paper introduces Culturescope, a mechanistic interpretability method to analyze internal cultural biases in large language models, revealing how biases like Western dominance emerge and vary across cultures.

Contribution

We propose Culturescope, a novel interpretability technique that probes LLMs' internal representations of cultural knowledge and biases, advancing understanding of cultural biases in AI models.

Findings

Low-resource cultures are less affected by biases.

Cultural biases like Western dominance are internally represented.

Biases emerge during model internalization processes.

Abstract

The growing deployment of large language models (LLMs) across diverse cultural contexts necessitates a deeper understanding of LLMs' representations of different cultures. Prior work has focused on evaluating the cultural awareness of LLMs by only examining the text they generate. This approach overlooks the internal sources of cultural misrepresentation within the models themselves. To bridge this gap, we propose Culturescope, the first mechanistic interpretability-based method that probes the internal representations of different cultural knowledge in LLMs. We also introduce a cultural flattening score as a measure of the intrinsic cultural biases of the decoded knowledge from Culturescope. Additionally, we study how LLMs internalize cultural biases, which allows us to trace how cultural biases such as Western-dominance bias and cultural flattening emerge within LLMs. We find that low-resource cultures are less susceptible to cultural biases, likely due to the model's limited parametric knowledge. Our work provides a foundation for future research on mitigating cultural biases and enhancing LLMs' cultural understanding.