Here is how to democratize AI with Open Multilingual Models.
Most advanced language models mainly serve English-speaking audiences in a world increasingly influenced by AI-powered communication. As it stands, many European languages are not included. Inclusion and diversity are ever-increasing staples in our modern world, and to broaden this AI innovation course, more languages should be included in software and programs.
Understanding Large Language Models (LLMs)
Large language models (LLMs) are advanced AI systems capable of processing and generating text, making them essential for applications like chatbots, translation, and content creation. These models are designed to comprehend and produce human-like language by training on vast datasets, enabling them to answer questions, complete sentences, and even generate essays or stories. Their key function lies in predicting the next word in a sequence based on the context provided by previous words. The potential of LLMs continues to grow, offering new possibilities for automating knowledge discovery—a task traditionally performed manually—through data-driven insights. With recent advancements, LLMs can now perform complex tasks such as reasoning, coding, and interacting with external tools, paving the way for fully autonomous discovery systems. Yet, their effectiveness in real-world scenarios is still under evaluation.
Communication Barriers in AI-Language Models
A pioneering study on AI-powered communication models – which, as we said, are mainly English-focused – introduced the EuroLLM project. This project aims to develop open-weight multilingual LLMs capable of understanding and generating text in all official European Union languages and other relevant languages, like Arabic and Korean. Open science is fundamental, and we need to start addressing the haps by existing LLMs, making them more accessible (Martins et al., 2024).
Large language models (LLMs) that work in many languages, not just English, are important for creating an inclusive and fair technology landscape. Language is more than just a way to communicate; it reflects the culture and identity of its speakers. When AI systems focus mostly on English, they leave out much of the world’s population, making it harder for many people to use and engage with these technologies. Multilingual LLMs can help everyone feel included in the digital world. Not everyone speaks English, and even those who do might prefer to use their native language for tasks that require more clarity or emotional connection.
For example, if a company’s help chatbot only speaks English, many non-English-speaking customers might have trouble understanding it, leading to mistakes. By offering technology in multiple languages, we can ensure that people from different language backgrounds receive the same level of service and support, making it easier for everyone to use and enjoy these tools. Multilingual LLMs play an essential role in preserving and revitalizing minority or endangered languages. As these languages struggle to survive in a world dominated by global media and communication, AI tools trained in less widely spoken languages will offer a valuable chance to keep them relevant in the digital age. When communities see their native languages included in advanced technologies, it will reinforce their cultural identity and motivate them to continue using and passing these languages on to future generations.
Including multiple languages will also help create fairer, less biased AI systems, as models trained mainly in English often reflect the cultural norms, beliefs, and biases present in English-language content. Extending LLM training to cover a wider range of languages allows developers to grasp more diverse ways of thinking, helping to reduce bias and provide a more balanced perspective of the world. Supporting many languages in LLMs is not only socially important but also practical. Multilingual AI allows businesses and public institutions to offer personalized services in different languages, opening new opportunities and better helping diverse populations. This is especially important in multilingual societies, where relying on just one predominant language may leave some groups excluded.
Lastly, having multilingual large language models (LLMs) will significantly advance AI research. Addressing the complexities of processing multiple languages—particularly those with limited resources—drives researchers to innovate and improve language models, eventually making AI more powerful and versatile.
Takeaways
Multilingual language models (LLMs) are an important step toward making AI fair and accessible to everyone, no matter what language they speak. Today, many AI tools focus only on English, which can exclude people who speak other languages and limit their ability to benefit from technology. Projects like EuroLLM show that it is possible to build AI systems that support many languages, promoting inclusion, fairness, and the preservation of different cultures. By using AI in multiple languages, businesses, governments, and communities can improve communication and provide better services for everyone. Supporting more languages also helps researchers find new ways to improve AI, working towards a better digital future.
