OMM Newsroom
As artificial intelligence becomes part of everyday life, languages with fewer speakers face a growing risk of falling behind in technology. For Georgian, spoken by about 4 million people worldwide, one of the main challenges is limited access to quality language materials for training AI systems.
According to AnewZ, the issue is not that AI cannot process Georgian. Compared with English, Spanish and Chinese, there is far less high-quality training material available for Georgian.
Georgian belongs to the Kartvelian language family and has a complex grammatical structure. Its features include extensive verb conjugation, grammatical cases and flexible sentence structure. Because a single verb can convey information about several participants in an action, systems trained mainly on other languages may struggle to generate natural text in Georgian.
Tbilisi-based Business and Technology University (BTU) announced on November 9 that it had developed an expanded database containing about 150 million tokens and more than 6 million language records, grammar examples and learning tasks. A token is a unit of text processed by an AI model; it may be a whole word or part of one.
The resource is intended for researchers and developers seeking to train, test and improve AI systems that work with Georgian. It includes a range of grammatical structures, word forms, contextual examples and tasks designed to teach systems the language’s features.
The project was developed with support from Palitra Group, which provided a large volume of Georgian-language text. The resource is available on GitHub. Under its published licence, it may be used for non-commercial purposes with attribution; commercial use requires separate permission.
Other initiatives are also addressing the shortage of technology resources for Georgian. Researchers at Tbilisi State University have developed GeoLogicQA, a Georgian-language evaluation dataset designed to test whether AI models can do more than recognise words and produce fluent sentences.
GeoLogicQA assesses logical reasoning, reading comprehension, general knowledge and mathematical problem-solving. In initial tests of several large AI models, including Gemini, DeepSeek, Grok and GPT-4o, accuracy ranged from 64% to 83%. Researchers said the models could produce satisfactory results but struggled with complex reasoning and multi-step questions.
Work is also underway to improve speech recognition for Georgian. Mozilla’s open-source international Common Voice initiative has collected hundreds of hours of Georgian-language recordings for speech-recognition research. As of September 2026, the Georgian dataset contained about 494 hours of audio, of which more than 262 hours had been validated.
These initiatives address different aspects of the same challenge: AI must not only recognise written Georgian but also understand, interpret and process spoken language in ways suited to everyday use.
A shortage of language resources affects more than awkward translations and unnatural chatbot responses. As AI becomes more integrated into education, public services, healthcare, business and information platforms, the quality of language support will affect how easily and effectively people can use these technologies.
Better language models for Georgian could eventually enable more accurate translations, educational tools that explain complex subjects in Georgian, improved speech recognition and digital services that communicate naturally with citizens. However, publishing a dataset does not automatically improve existing AI platforms. The material must first be incorporated into training or evaluation processes, and its effectiveness must be independently assessed.
The quality and diversity of the material, as well as the accuracy of its grammatical annotations, are as important as its size. Written-language resources do not capture every feature of Georgian: regional speech, dialects, informal conversation, cultural references and specialised terminology must also be considered. Building reliable AI systems requires continually collecting, checking and updating such material. BTU’s announcement is another step in that direction, not a final solution.
A database the university previously made publicly available contained about 12 million tokens and more than 645,000 structured records. These figures reflect the project’s development in stages.
The expanded figure of 150 million tokens announced by the university will need to be reflected in public documentation and independently evaluated as the new version becomes available.
For Georgian, a language spoken by millions but relatively underrepresented in the global technology industry, the issue is not just teaching machines to form sentences in Georgian. It is also about ensuring the language can be used to learn, work, communicate and participate in a world increasingly shaped by AI.
