Polish & AI‑Prompts: Why One Unexpected Language Out‑performed English

What the Study Found

A recently released benchmark study of multiple LLMs (from major providers) tested how well different languages perform when used as input (prompts) in long‑context, complex tasks (i.e., very large input size, many tokens, high reasoning demand). According to the study:

• Polish ranked first among 26 languages tested when models were given prompts with long context lengths (64K and 128K tokens) and complex tasks, achieving approximately 88 % average accuracy.
• English, often regarded as the “default” language for AI, ranked only sixth (≈ 83.9 %).
• Chinese, despite being a major language in AI research and large datasets, performed poorly comparatively (≈ 62.1 %).
• Other top performers included Russian, French, Italian, Spanish (grouped behind Polish). On the lower end were languages such as Sethoto (a Bantu language), Tamil and Swahili.
• The researchers noted that script family (Latin/Cyrillic) and whether the language is “high‑resource” (lots of data) vs “low‑resource” influenced performance—but the Polish result was still surprising given its relatively smaller training‑data volume.

Why It Matters

• Prompt engineering is increasingly central: how you phrase input to an AI determines output quality. That a non‑anglophone language scored top suggests that language choice matters more than assumed.
• Language equity in AI: If a “less dominant” language outperforms English, it challenges assumptions about data‑volume supremacy. This could influence how AI is deployed in multilingual societies and how language‑models are developed.
• Model design & evaluation: The finding may prompt AI developers to pay more attention to linguistic structure, morphology and diversity, not just big language‑data sets.
• National/regional AI strategy: For countries whose languages are not English, this result may provide impetus to invest in language‑specific AI resources, corpora and models.

What the Study Adds & What Was Not Fully Covered

Adds:

• A new benchmark focusing on very long‑context prompts (tens of thousands of tokens) rather than typical short prompts or Q&A.
• Highlights the gap between “high‑resource” language expectation and actual performance in certain tasks.
• Raises questions around the role of language structure, tokenisation efficiency, syntax, morphology and how they affect LLM performance.

Gaps / Not fully covered:

• Why exactly Polish excelled: The study mentions plausible factors (language family, script type, linguistic structure) but does not conclusively isolate which contributed most. For example, is Polish’s rich morphology beneficial for context tracking?
• How generalisable: The tasks were specific (very long‑context, complex tasks). It is unclear if Polish also out‑performs for other typical LLM tasks (short prompts, casual conversation, image‑text tasks).
• Data‑volume paradox: Polish has less training data compared with English or Chinese; how did models compensate? Was transfer‑learning from English data effective? The study only speculates.
• Impact on languages outside tested 26: Many languages (especially lower‑resourced, non‑Latin script) were not included or had too little data for meaningful comparison.
• Model architecture interaction: The study used several LLMs from different vendors, but it is less clear how architecture, pre‑training focus, regional fine‑tuning, and tokenisation differences influenced language ranking.
• Prompt engineering in Polish: For non‑Polish native users, using prompts in Polish may not be feasible—so practical implications for multilingual users are not fully addressed.
• Implications for end‑users: Many users prompt LLMs in English out of habit; how should they adapt? The study doesn’t provide guidelines.
• Commercial and ethical implications: If Polish is more “prompt‑efficient,” might this advantage get exploited commercially (e.g., Polish‑based AI services) or lead to unintended bias? Not deeply explored.
• Effects on cross‑language models: How does a well‑performing language in prompts affect translation tasks, cross‑lingual models or multilingual assistants?
• Real‑world deployment: The study is lab‑based; how this translates to real‑world applications (chatbots, customer service, multilingual AI assistants) remains to be seen.

Possible Explanations for Polish’s Performance

Here are several hypotheses emerging from the field:

1. Morphological richness & inflection: Polish uses inflection, many grammatical cases, free word‑order. This may force models to “pay attention” more to context, thus enhancing algebraic tracking of long context.
2. Tokenisation benefits: Because Polish is “denser” (information‑packed) per token, the model might achieve effective context utilisation more efficiently.
3. Training data nuance: While Polish may have fewer raw data‑points, the data may be more curated or consistent—plus the model may benefit from cross‑training on related Slavic languages (Russian, Czech) improving transfer.
4. Script & language family effect: Languages in Latin or Cyrillic script (Slavic, Romance, Germanic) seemed to outperform scripts with large orthographic distances.
5. Benchmark design: The tasks were specifically “long‑context, complex”—which may favour languages like Polish whose grammar encourages precise chain‑of‑thought. If the tasks were different, results might vary.
6. Prompt engineering in native language: Since Polish prompts may be crafted by Polish‑speaking researchers with high‑quality instructions, prompt‑quality may have been higher; this may bias results if English prompts were less optimally crafted.

Implications for Developers, Businesses & Users

• Developers: Consider bolstering data resources for languages that appear “under‑used” but high‑potential; refine tokenisers and architectures for languages with dense morphology.
• Businesses: If your service involves multilingual AI assistants or translation, consider testing whether prompts in “less common” but high‑efficacy languages improve performance or reduce cost.
• Users: If you are comfortable in multiple languages, you might experiment with prompting in different languages to see which yields better output quality for your applications.
• Policy & education: Countries with non‑English major languages might invest strategically in AI language models, corpora and infrastructure—recognising that the “language advantage” in AI may be more open than assumed.

Risks, Considerations & Ethical Questions

• Equity of language‑access: If certain languages prove more “efficient” for AI, this could lead to an imbalance where those languages gain undue advantage in AI development, potentially leaving other language communities behind.
• Bias and dataset dominance: Languages previously considered “low‑resource” may now attract attention, but still may be under‑represented in corporate AI training—raising questions of fairness.
• Prompt misuse: If one language is known to yield better results, prompt engines may push subtle bias (e.g., pushing users to adopt certain languages).
• Practicality for multilingual users: Many global users know English only; expecting them to switch languages for better prompts may be impractical.
• Copyright and cultural data exploitation: The data that gave Polish advantage may have specific licensing/corpus conditions; replication in other languages may face legal or ethical constraints.

FAQs: Common Questions & Answers

Q1. Does this mean I should always write prompts to AI in Polish to get the best results?
Not necessarily. The study shows Polish performed best in this specific long‑context benchmark. For everyday use (short prompts, English‑based workflows) English may still be optimal due to familiarity, existing tools and larger ecosystems. If you are proficient in Polish, it’s worth experimenting; if not, choosing a language you know well and where prompt‑quality is high may be more important.

Q2. Why did English not come out on top, given it has more training data and resources?
The hypothesis is that for tasks involving very long contexts, languages with certain structural properties may allow LLMs to manage complexity more efficiently. English may suffer from ambiguity, non‑inflection, or token inefficiency when the context scale becomes extreme. Additionally, the prompt quality and domain of training may differ. The study suggests the amount of data isn’t the only factor.

Q3. Can this finding apply to other “under‑resourced” languages too?
Possibly. The finding indicates that language‐effectiveness isn’t strictly tied to speaker count or data‑volume. What matters includes grammar structure, tokenisation efficiency, and the quality of prompt engineering. Other languages with rich morphology and good resources may also outperform expectations—but each must be tested.

Q4. What kind of tasks were used in the benchmark?
The tasks were long‐context (64K and 128K token inputs), involving complex reasoning, logic, text synthesis or summarisation over large input spans. They were not simple question‑answer tasks. This means the advantage might apply particularly for “deep thinking” prompts rather than conversational chat.

Q5. Should AI developers focus more on Polish or similar languages now?
It suggests that yes—investing in well‑structured corpora, developing tokenisers, fine‑tuning models for languages like Polish may yield high returns. But developers should also ensure broad multilingual inclusion; focusing solely on Polish would be a narrow strategy.

Q6. Does this mean English becomes less important for AI?
Not at all. English remains hugely important due to its wide use, large ecosystems and tooling. This finding adds nuance: for some tasks and contexts, other languages may outperform English—but English will continue to dominate many practical applications.

Q7. Are translation or multilingual models affected by this?
Yes. If a language performs better in prompting, it may affect translation quality, cross‐language alignment and model architecture choices. Multilingual or translation‐engine developers may need to reconsider weighting, tokenisation and fine‑tuning strategies for different languages.

Q8. What should a non‑Polish speaker take away from this?
If you are using LLMs, consider experimenting with other languages you know – even ones less common in AI. Pay attention to how your prompts are structured. The language of the prompt matters, but so does prompt clarity, specificity and context. Language choice is one variable among many.

Q9. Are these findings final or could they change?
They are preliminary. The study addresses a specific benchmark under certain conditions. As LLMs evolve, training data expands, and new architectures come online, results may change. So this is an interesting indicator rather than a definitive rule.

Q10. What are the next research questions?
Key directions include: (1) testing across more languages and scripts (especially non‑Latin ones), (2) understanding why certain languages succeed (linguistic structure vs data vs tokenisation), (3) applying the findings to real‑world applications (chatbots, translation, document analysis) and (4) measuring long‑term effects of language choice in multilingual AI systems.

Final Thoughts

The revelation that Polish may top artificial‑intelligence prompting performance shakes up some assumptions. It reminds us that AI isn’t just about “more data in English,” but about how languages interact with model architecture, tokenisation, structure and prompt quality. For users, developers and policy‑makers alike, this offers a richer horizon: multilingual AI isn’t only a matter of support—it’s an opportunity for insight, innovation and more equal participation across languages.

Sources Euro News