LLMs and Linguistic Diversity: An Equity Issue

A recent study published on arXiv highlights a crucial issue in the field of large language models (LLMs): the disparity in the quality of responses based on the language used.

The research shows that users who interact with LLMs in low-resource languages consistently receive lower quality responses compared to those who use more common languages. This raises important questions of equity and accessibility.

The Influence of Cultural Context

The study also delves into the link between language and culture within LLMs. The results indicate that the choice of language not only influences the quality of the response but also the cultural context used by the model to generate it. This variation in cultural context has a direct impact on the relevance and accuracy of the information provided.

To evaluate this aspect, the researchers used a translated benchmark, CulturalBench, in several languages, confirming that the cultural context varies significantly depending on the input language.

These results underscore the need to develop more inclusive LLMs capable of handling linguistic and cultural diversity fairly and accurately.