Asking the same question might seem like it should elicit the same answer, but multilingual AI models don't always handle languages in the same way. Differences can arise in the accuracy of information, the choice of terminology, the details the model retrieves, and even its understanding of the question's cultural context. However, this doesn't necessarily mean the model is better at thinking in one language than another; rather, it's related to how it's trained and how it represents languages within its structure.
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The data determines the starting point.
Large language models rely on huge amounts of text during the training phase, but this data is not distributed equally among languages. English and languages with a large digital presence usually have more textual resources, while other languages suffer from a lack of high-quality data.
Other studies support this picture. One study that examined more than 250 languages found that adding multilingual data can improve the performance of low-resource languages, but the benefit depends on the size of the data and the linguistic similarity between languages, and excessive expansion in the number of languages may impose limitations on the model's capabilities.
The method of word segmentation is important
Before the model can process the question, words are usually converted into smaller units called symbols or tokens, and this process is not identical in languages.
Recent research has shown that the choice of a text-to-code tool can affect the model's performance and training costs, and that using tools designed in an English-centric way may lead to a significant decline in performance when dealing with other languages, due to inefficient text representation.
A study presented at the 2025 Society for Computational Linguistics conference also found that text segmentation is affected by linguistic diversity, and that these differences can impact the performance of language models in various tasks. This means that two questions with identical meanings may not reach the model in a computationally identical form.
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