Artificial intelligence has made impressive progress in understanding and generating language. But while models like GPT-4 handle English and other widely used languages well, they struggle with low-resource languages—those with limited digital text or training data. Understanding why this happens is crucial for building more inclusive AI.

What Are Low-Resource Languages?

A low-resource language lacks large, high-quality digital datasets. This can include regional languages, indigenous languages, or dialects. Examples include Wolof, Quechua, or Breton.

Because LLMs rely on huge corpora of text to learn patterns, these languages are underrepresented in the training data, limiting model performance.

Why AI Struggles

• Data scarcity: Fewer books, articles, and websites in these languages
• Dialects and variations: Models may confuse regional forms or mixed usage
• Lack of standardization: Spelling and grammar rules may vary, creating inconsistency
• Cultural nuance: Without exposure, AI may misinterpret idioms or traditions

These challenges lead to inaccurate translations, incomplete answers, or biased outputs for low-resource languages.

Why It Matters

• Digital inequality: Speakers of low-resource languages risk exclusion from AI-driven services.
• Bias amplification: LLMs defaulting to English or mistranslating can marginalize these languages further.
• Preservation stakes: Without careful AI adaptation, some languages remain digitally invisible.

Collaborations with linguists, native speakers, and local institutions are key to bridging these gaps and creating fairer AI systems.

Low-resource languages reveal an imbalance in today’s AI models: while powerful, they are not yet inclusive of the world’s linguistic diversity. Addressing this requires to be targeted fine-tuning, human review, and linguist involvement to make AI truly multilingual and equitable.

Low-resource languages highlight another critical issue: how bias appears across gender, dialects, and cultures in multilingual AI.
Our next article explores bias in multilingual AI, how it forms, why it matters, and how to reduce it.

👉 Read next: Bias in Multilingual AI: Gender, Dialects, and Culture