Neuron-Level Analysis of Cultural Understanding in Large Language Models

As large language models (LLMs) are increasingly deployed worldwide, ensuring their fair and comprehensive cultural understanding is important. However, LLMs exhibit cultural bias and limited awareness of underrepresented cultures, while the mechanisms underlying their cultural understanding remain underexplored. To fill this gap, we conduct a neuron-level analysis to identify neurons that drive cultural behavior, introducing a gradient-based scoring method with additional filtering for precise refinement. We identify both culture-general neurons contributing to cultural understanding regardless of cultures, and culture-specific neurons tied to an individual culture. These neurons account for less than 1% of all neurons and are concentrated in shallow to middle MLP layers. We validate their role by showing that suppressing them substantially degrades performance on cultural benchmarks (by up to 30%), while performance on general natural language understanding (NLU) benchmarks remains largely unaffected. Moreover, we show that culture-specific neurons support knowledge of not only the target culture, but also related cultures. Finally, we demonstrate that training on NLU benchmarks can diminish models' cultural understanding when we update modules containing many culture-general neurons. These findings provide insights into the internal mechanisms of LLMs and offer practical guidance for model training and engineering. Our code is available at https://github.com/ynklab/CULNIG LLMs are rapidly spreading throughout the world with their ability to solve various tasks. Our world is culturally diverse, and our knowledge, commonsense, and values are not always universal. LLMs must possess cultural understanding to be deployed fairly and prevent cultural inequity. However, several studies have pointed out that LLMs, which are mainly trained on English-dominant corpora, often exhibit culture-related biases, generating outputs skewed toward certain highly represented cultures (Naous et al., 2024; Myung et al., 2024; Sukiennik et al., 2025). In order to evaluate the cultural understanding of LLMs, a number of benchmarks have been constructed (Myung et al., 2024; Chiu et al., 2025; Rao et al., 2025; Zhao et al., 2024, inter alia). Additionally, some methods have been proposed to enhance cultural awareness of LLMs (Li et al., 2024a;b; Liu et al., 2025).