arXiv:2606.17688v1 Announce Type: new Abstract: Recent work has shown that LLMs overrepresent dominant cultures, particularly Western ones, while marginalizing others. We investigate whether this affects models' ability to generate culturally adapted responses by evaluating their use of local measurement units based on the user's perceived cultural background. We introduce Cultural and Pragmatic Response Inference (CAPRI), a dataset of conversations with varying levels of cultural cues. Experiments with state-of-the-art LLMs show that models can infer cultural background and recall relevant co
The proliferation of Large Language Models (LLMs) and increasing scrutiny of their biases are driving immediate research into their cultural representation and applicability.
This research highlights a critical limitation in LLMs regarding cultural intelligence, which is essential for global adoption and effective human-AI interaction in diverse contexts.
Our understanding of LLM capabilities is refined, moving beyond mere language generation to a more nuanced view of cultural applicability; it suggests commercial and ethical implications for their deployment.
- · AI ethics researchers
- · Developers focused on culturally aware AI
- · Regions with often-marginalized cultural contexts
- · Companies deploying 'one-size-fits-all' LLMs globally
- · LLMs with inherent Western biases
Further research and development will focus on integrating diverse cultural contexts more effectively into LLM training and fine-tuning.
This could lead to the development of specialized, culturally-aligned LLMs or modular cultural adapters for existing models, impacting market segmentation.
Enhanced cultural adaptability could significantly accelerate AI adoption in non-Western markets, potentially decentralizing the dominant AI technology landscape.
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Read at arXiv cs.CL