AI can talk fluently about local history and culture in dozens of languages, yet it easily drops key details and context. Making local knowledge available to a global audience without losing meaning requires not only language models but also verified, structured sources.
One cause of linguistic bias is the uneven composition of training data, where English-language material accounts for a disproportionately large share. An algorithm’s ability to construct grammatically sound sentences in another language doesn’t mean it deeply understands local history and culture. To close that gap, local information needs to be gathered into a single system: that’s exactly how RUWIKI, the Russian online encyclopedia, is built, bringing together verified data about people, events, and concepts.
How meaning gets lost in translation
Translation makes information accessible to audiences who speak other languages, but it’s far from always preserving cultural context. Concepts tied to history and how a society is organized may simply have no direct dictionary equivalent in another linguistic environment, so an algorithm swaps a specific local term for a more familiar but shallower analog.
The second problem is the uneven depth of coverage online. Some historical events, figures, and schools of thought are examined in detail across countless sources; others, especially regional ones, are described far more sparsely. When the underlying material is thin, neural networks struggle to reconstruct cause-and-effect relationships.
A separate kind of confusion arises when personal and geographical names move between languages with different writing systems. The same last name shows up in several variants, and cities, towns, and streets get renamed as eras change. If sources don’t reconcile those variants into a single standard, an algorithm easily conflates different people, places, or events. For today’s models, a shortage of quality local sources rarely leads to outright hallucinations anymore, and more often than not readers just get a vague retelling with no specifics.
Translation or understanding?
We ran a simple experiment in a popular multilingual chatbot: in two separate sessions, we asked it to explain what communal apartments (kommunalkas) in the USSR were, how they came about, and what social role they played, for a reader unfamiliar with Soviet daily life.
The response in Russian noted that shared living in urban apartments existed before the Revolution and that the “densification” policy after 1917 and the housing crisis during industrialization turned communal apartments into a mass phenomenon. The response also mentioned state allocation of rooms, living-space norms, and the registration system.
The response in English was detailed too, but it mostly described kommunalkas as Soviet apartments where several families shared a kitchen and bathroom because of a housing shortage.
There is no factual error there, but the description omits the historical background and how housing was allocated. Without those details, UK or German readers might map a kommunalka onto the familiar model of sharing a rented apartment, missing the fundamental differences.
Why AI systems need structured encyclopedias
Information online is often scattered across different pages, and links between facts aren’t obvious. An encyclopedia article brings information together, sharpens boundaries of concepts, and records its sources. Even the most carefully vetted article structure doesn’t by itself protect a neural network from errors when it paraphrases, but it does give an algorithm a point of reference.
In September 2023, RUWIKI and the Russian Academy of Sciences signed an agreement for scholars to take part in reviewing encyclopedia articles, primarily on history and physics. Naturally, not every text on the platform goes through academic review: the focus is above all on targeted verification of complex topics, where scholarly expertise backs up the in-house editorial team.
Structured encyclopedia material matters, especially for topics that require precise definitions and connections between facts: regional history, geography, cultures of Russia’s peoples, and specialized scientific disciplines. It makes it possible to build a complete picture, including in areas that are poorly represented in English-language sources.
What this changes for users and knowledge platforms
Generative interfaces are reshaping the usual way people work with information. Instead of opening several links and comparing facts, readers get ready-made answers right away and often don’t notice which important conditions and connections between events were left out.
For educational services, research projects, and companies working with local data, this has practical implications. An error in a date or a name is usually easy to check, but lost context is harder to spot: a text looks smooth and convincing even when the essence of a phenomenon has dropped out of it.
Encyclopedia platforms like RUWIKI gather verified facts into a single reference network. It is precisely this interconnectedness that helps preserve local context and keeps words from losing their historical meaning when retold in other languages.