Should My Model Speak My Language? - Karam Veer Kanwar

As AI models grow more powerful, a quieter change is occurring: the emergence of country-specific language models. These AI systems are trained not on the entire internet, but on datasets that reflect a nation’s languages, cultural norms, government records, and user behavior.

Unlike large, general-purpose models like ChatGPT or Gemini, local LLMs aim to capture the voice and values of a specific area. They are not necessarily better or smarter, but they are more attuned. In nations with multiple official languages, strong privacy laws, or unique digital habits, this focus is important.

India is at the forefront, with companies like Sarvam AI and Krutrim creating multilingual models designed for Indian languages and user contexts. Government efforts like Bhashini are promoting digital access across many regional dialects. In Latin America, the launch of Latam-GPT will cover Spanish, Portuguese, and Indigenous languages. The UAE’s Falcon model and Africa’s Swahili-focused projects illustrate this global trend.

The performance gap is significant. A recent benchmark by AI4Bharat showed that Indian language models like IndicBERT outperformed English-based models by up to 32% in regional comprehension tasks. In multilingual assessments, localized LLMs demonstrated 50 to 70% higher accuracy when responding to questions phrased in native idioms or cultural references.

Why do these models exist? Several reasons stand out. First, language access: most global models struggle with languages that aren’t widely used, such as those outside of English. Second, regulatory pressure: countries prefer AI built within their borders, using their data and adhering to their laws. Third, market context: local models can be fine-tuned to grasp domestic platforms, slang, and customer behavior in ways global models cannot.

There are trade-offs. Many country-specific models are smaller, newer, and less capable overall. However, they don’t need to do everything—just a few things well. For governments, this could mean better public services. For businesses, it might lead to smarter customer support or localized product suggestions.

What is developing is not a replacement for global AI but a complement. Consider it like the internet: one global network and thousands of local sites. As AI spreads worldwide, it is starting to speak with a local accent, and that’s not just interesting; it’s inevitable.

What local models need to succeed:

- Access to high-quality, diverse, and representative local datasets.

- Strong partnerships among governments, startups, and academia.

- Evaluation with localized metrics, not just those in English.

As AI becomes a key part of digital infrastructure, it’s no longer enough for models to be powerful. They also need to be relevant. Country-specific LLMs aren’t aimed at outpacing ChatGPT or creating the next trillion-parameter model. They focus on connecting with people in their own language, considering their context, and following their laws. Whether you're a policymaker, a startup, or a multinational company, understanding this change is essential. The future of AI isn’t just global; it’s becoming more local as well. Those who can communicate in both ways will succeed.

About me Hey! My name is Karam Veer Kanwar, and I am a final-year finance student at UBC Sauder. 

I have a background working with startups, consulting firms, tech companies, and non-profits, and I enjoy working across industries and disciplines.