arXiv:2606.07422v1 Announce Type: cross Abstract: Large language models are increasingly used to answer culturally grounded questions across languages, yet it remains unclear whether local cultural knowledge is better accessed through English or the local language. Existing evaluations face two key limitations: many rely on parallel template-based questions that may not reflect how cultural knowledge naturally appears, and raw accuracy conflates general language proficiency with language-conditioned knowledge access. We address these issues with a controlled framework built on real-world cultu
The proliferation of Large Language Models (LLMs) globally necessitates a deeper understanding of their cultural knowledge access, especially as their use cases expand into non-English speaking contexts.
This research provides a framework to assess how well LLMs handle cultural nuances in different languages, crucial for developing culturally competent and globally applicable AI systems.
The understanding of whether local cultural knowledge in LLMs is better accessed through English or local languages shifts, impacting model training and deployment strategies for global markets.
- · AI developers targeting non-English markets
- · Multilingual content creators
- · Cultural preservation efforts via AI
- · Monolingual LLM development strategies
- · Platforms with poor cultural localization
- · Generative AI limited by cultural biases
Improved evaluation metrics for assessing cultural knowledge in multilingual LLMs become standard.
Demand for diverse, culturally rich datasets in local languages increases, fueling data collection and annotation efforts.
Enhanced cultural understanding in AI leads to more effective and equitable global AI adoption, potentially empowering marginalized language communities.
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Read at arXiv cs.AI