Study Identifies Polish as Most Effective Language for AI Prompts
In a groundbreaking study conducted by the University of Maryland (UMD) in collaboration with Microsoft, researchers found that Polish is the most effective language for prompting artificial intelligence (AI) systems. The findings challenge the common belief that English, the most widely used language in AI development, would naturally perform best. Surprisingly, English ranked only sixth out of 26 languages evaluated.
“Our experiment yielded some surprising and unintuitive findings,” stated the report’s authors. “English did not perform best across all models; in fact, it came sixth when long texts were assessed, while Polish proved to be the leading language.”
How the Study Was Conducted
The research team tested several prominent AI language models, including OpenAI’s ChatGPT, Google Gemini, Qwen, Meta’s LLaMA, and DeepSeek. Each model was given identical prompts translated into 26 different languages to assess how accurately they completed the tasks.
The evaluation focused on a range of tasks, including summarization, translation, and question-answering capabilities. The researchers then measured the accuracy of responses for each language, revealing some unexpected outcomes.
Polish Scores Highest in Accuracy
Polish emerged as the most effective language, achieving an average accuracy of 88% across all tasks. This performance is particularly notable given that Polish is often considered one of the most difficult languages to learn due to its complex grammar and pronunciation rules.
In a Facebook post, the Polish Patent Office remarked, “As the analysis shows, it is the most precise in terms of giving commands to artificial intelligence. Until now, Polish was widely regarded as one of the most difficult languages to learn. As it turns out, humans have trouble with it, but not AI.”
Interestingly, AI models demonstrated a strong understanding of Polish despite the limited amount of Polish-language data available for training compared to languages like English and Chinese. This suggests that the structure and clarity of the Polish language may help AI models interpret and execute prompts more effectively.
Top 10 Languages for AI Communication
The study ranked the top 10 languages that yielded the most effective results when interacting with AI models:
- Polish — 88%
- French — 87%
- Italian — 86%
- Spanish — 85%
- Russian — 84%
- English — 83.9%
- Ukrainian — 83.5%
- Portuguese — 82%
- German — 81%
- Dutch — 80%
These rankings indicate that Romance and Slavic languages tend to perform well when used to prompt AI systems, with Polish setting the benchmark.
Chinese and Other Languages Lag Behind
In contrast to Polish’s high performance, Chinese ranked near the bottom of the list, placing fourth from the end among the 26 languages tested. This was particularly surprising given the extensive availability of Chinese-language training data and China’s significant investment in AI research and development.
This contrast raises questions about how different linguistic structures may influence AI comprehension and task execution. The complexity of Chinese characters and syntax may hinder some models’ ability to interpret instructions effectively.
Implications for AI Development and Localization
The findings could have significant implications for the development and localization of AI systems. Developers and companies aiming to improve AI performance may reconsider their language choices when designing prompts or training datasets.
Moreover, the results emphasize the need for a more nuanced understanding of how language affects AI behavior. As AI becomes more integrated into global communication, ensuring that systems perform reliably across multiple languages is becoming increasingly important.
“This study challenges the long-standing assumption that more data equates to better performance,” a UMD researcher noted. “It opens new doors for exploring how linguistic structure, rather than data volume alone, influences AI effectiveness.”
Future Research Directions
The researchers plan to expand their investigation to include other languages and AI models. They also aim to analyze how cultural context and idiomatic expressions impact AI comprehension and response accuracy.
This continued research could lead to more inclusive and effective AI systems capable of understanding and interacting in a broader range of languages and dialects.
This article is inspired by content from Original Source. It has been rephrased for originality. Images are credited to the original source.