Another challenge for multilingual models comes from disparities in the amount of data they train on in each language. When analyzing content in languages they have less training data for, the models end up leaning on rules they have inferred about languages they have more data for. This hampers their ability to understand the nuance and contexts unique to lower-resource languages and imports the values and assumptions encoded into English. One of Meta’s multilingual models, for instance, was trained using nearly a thousand times more English text than Burmese, Amharic, or Punjabi text. If its understanding of those languages is refracted through the lens of English, that will certainly affect its ability to detect harmful content related to current events playing out in those languages, like the Rohingya refugee crisis, the Tigray war, and the Indian farmers’ protest.

Finally, even if a multilingual language model were trained on equal amounts of high-quality data in every language, it would still face what computer scientists call the “curse of multilinguality”—that is, languages interfere with one another in the ultimate outputs of a model. Different languages compete with each other for space within a multilingual language model’s internal mapping of language. As a result, training a multilingual model on more Hindi data may hurt its performance on tasks in etymologically distinct languages like English or Tagalog, and increasing the total number of languages a model trains on may hurt its performance in all of them.

In the case of content moderation, this raises difficult questions about which languages social media companies should prioritize, and what goals these models should target. Should multilingual language models try to achieve equal performance in all languages? Prioritize ones with the most speakers? The ones facing the most dire content moderation problems? And who decides which are the most dire crisis?

Multilingual language models promise to bring the analytical power of LLMs to all the world's languages, but it is still unclear whether their capabilities extend to detecting harmful content. What is harmful does not seem to be easily mapped across languages and linguistic contexts. To make sure these models do not lead to disparate impacts on different language communities, social media companies need to offer more insight into how these models work.

At a minimum, companies should share information about which products rely on these models, what kinds of content they're used on, and in what languages they are used. Companies should also share basic metrics on how language models perform in each language, and more information about the training data they use, so researchers can evaluate those data sets for bias and understand the balance the company is striking between different languages. While the biggest companies, like Facebook and Google, do release versions of their language models to the public for researchers and even other companies to use, they are often mum about how those publicly available systems relate to or differ from those used in their own products. These proxies are not enough—companies should share information about the actual language models they use for content moderation as well.

Social media companies should also consider that a better approach may not be using one large multilingual model but multiple, smaller models more tailored to specific languages and language families. Lelapa’s AfroLM model, for instance, is trained on 23 different African languages and is able to outperform larger multilingual models in those languages. Research communities all over the world are working hard to figure out what kinds of language models work best for their own languages. Social media companies should draw not only on their technical work but on their expertise in local language context.

As a solution, multilingual language models run the risk of being a “rest of the world”-sized band-aid to a dynamic problem. By offering more transparency and accountability, prioritizing individual language performance over scalability, and consulting with language communities, companies can start dismantling that approach.