A recent study has challenged common assumptions about the languages that work best for AI, with Polish emerging as a surprising leader. In contrast, English, typically considered the lingua franca of technology, only ranked sixth among 26 languages.
Researchers from The University of Maryland (UMD) and Microsoft conducted a comprehensive experiment to test the effectiveness of various languages when interacting with AI models. The findings were unexpected, showing Polish outperformed others in language model accuracy, especially when processing long texts.
The study involved several leading AI platforms, including OpenAI, Google Gemini, Qwen, Llama, and DeepSeek, and tested them with identical inputs in 26 different languages. According to the research team, the results defied expectations, revealing that Polish achieved an impressive 88% accuracy rate, making it the most efficient language for AI tasks.
Although Polish has historically been viewed as a difficult language for humans to master, AI systems demonstrated a remarkable capacity to understand it. The Polish Patent Office highlighted this surprising outcome in a social media post, noting that while Polish may be challenging for learners, it is particularly well-suited for AI interactions.
On the other hand, Chinese, despite its prominence in global affairs, ranked poorly in the study, landing near the bottom of the list of tested languages. This contrasted sharply with the strong performance of European languages like French, Italian, and Spanish, which also scored highly.
The study’s results suggest that the smaller pool of Polish-language data may not be as significant an issue for AI models as previously thought. This discovery raises questions about how AI interacts with different languages and whether AI’s linguistic abilities could outpace human language acquisition.
Researchers speculate that the success of Polish could be linked to its rich grammatical structure, which may allow AI systems to parse commands with greater precision. While Polish’s complex morphology might hinder human learners, it appears to be an advantage for AI models trained to process nuanced linguistic patterns.
Additionally, the study has sparked discussions about the implications for language learning. If AI can better understand languages like Polish, could it accelerate the development of language tools that help people learn complex languages more effectively?
The findings highlight that AI’s understanding of languages can sometimes surprise experts and suggests that proficiency in certain languages might not align with human expectations. It also points to the potential for AI to redefine how languages are valued in a digital world.
Here are the top 10 languages for effective AI prompts:
- Polish 88%
- French 87%
- Italian 86%
- Spanish 85%
- Russian 84%
- English 83.9%
- Ukrainian 83.5%
- Portuguese 82%
- German 81%
- Dutch 80%
The results of this study highlight a significant shift in the way we should approach AI training. While English has long been seen as the go-to language for technology, Polish is showing that AI’s proficiency does not always mirror human patterns of language learning.
This opens up a wider conversation about the role of different languages in the digital landscape. As AI continues to evolve, we may see more languages from unexpected regions and cultures gaining prominence in AI systems. The effectiveness of Polish could also push other linguistic communities to rethink how their languages could contribute to the future of AI and machine learning.
