Beyond English: Polish Emerges as the Most Effective Language for AI Prompts, Study Reveals

A Surprising Shift in AI Communication: English Isn't King

In a development that has sent ripples through the AI research community, a comprehensive study has revealed that the English language, long considered the default for interacting with artificial intelligence, has been dethroned. Researchers from the University of Maryland (UMD), in collaboration with Microsoft, conducted an extensive analysis of 26 languages across leading AI models including OpenAI, Google Gemini, Qwen, Llama, and DeepSeek. The results are, to say the least, eye-opening: English isn't even in the top five for AI prompt effectiveness.

Polish Takes the Crown in AI Interactions

The study, which involved 5,200 tasks with varying context lengths from 8,000 to 128,000 tokens, aimed to understand how AI systems process both concise and extensive textual inputs. Contrary to popular belief, English landed in a surprising sixth place when evaluating long texts, falling behind languages like Polish, which emerged as the undisputed leader. Researchers noted a significant disparity, with Polish demonstrating an average accuracy of 88%. This placed it ahead of French (87%), Italian (86%), Spanish (85%), and even Russian (84%). The full list of top-performing languages includes: Polish (88%), French (87%), Italian (86%), Spanish (85%), Russian (84%), English (83.9%), Ukrainian (83.5%), Portuguese (82%), German (81%), and Dutch (80%).

The Paradox of Complexity and Clarity for AI

The findings challenge the notion that complex languages pose a significant hurdle for AI. In fact, the study suggests the opposite. "As the analysis shows, is the most accurate language in terms of issuing commands to artificial intelligence. Until now, Polish was considered one of the most difficult languages to learn. As it turns out, people have problems with it, but not AI," observed the Polish Patent Office on Facebook. This sentiment highlights a fascinating paradox: what humans find challenging, AI might find remarkably clear. The intricate grammatical structure of Polish, with its seven cases, three genders, and rich system of inflections, appears to aid AI in discerning meaning, particularly within lengthy sentences. The Latin alphabet, commonly used in Polish, also contributes to easier text processing by modern AI systems.

Data Quality Trumps Quantity in AI Training

A key insight from the research is the impact of data volume on AI performance. The study found that the longer the text input, the more pronounced the differences between languages became. While the accuracy gap between the best and worst languages was 11% for short queries, it widened to a striking 34% for very long ones. This suggests that AI models struggle with languages that have a smaller digital footprint, as they have less data to learn complex sentence structures and nuances. Ironically, Polish, with its comparatively smaller pool of accessible training data compared to titans like English or Chinese, outperformed them significantly. Chinese, despite its vast digital resources, ranked poorly, landing in fourth from the bottom among the tested languages. This phenomenon underscores a crucial principle in AI development: quality of data may be more critical than sheer quantity, especially when it comes to intricate linguistic structures.

The researchers were genuinely surprised by these results, especially given that Polish has a relatively small volume of training data. For comparison, English or Chinese have tens of times more publicly available sources. Despite this, the problem of Polish language with less data and lesser-known sources did not prevent it from taking first place.

The Future of Multilingual AI Interaction

While English remains a dominant force in global communication, this study signals a potential paradigm shift in how we interact with AI. The implications are far-reaching, suggesting that developers may need to reconsider their language priorities. Even with a smaller digital presence, Polish has demonstrated superior performance, driven by its inherent grammatical clarity and the quality of its available online content, including over 1.6 million Wikipedia articles. This suggests that as AI technology continues to evolve, a deeper understanding and integration of diverse linguistic structures could unlock new levels of efficiency and accuracy in human-AI collaboration. The era of English-centric AI might be giving way to a more multilingual and nuanced future.