Short answer. A high-resource language has large volumes of digitised, labelled and unlabelled data available for training AI. A low-resource language lacks that data regardless of how many people speak it. The distinction is about the supply of usable data, not speaker population. The standard reference is the six-class framework from Joshi and colleagues (ACL 2020), which places seven languages in the top class and 2,191 in the bottom one, and the gap widens on its own.
Key takeaways
- A language's resource level describes the stock of digitised text, audio, dictionaries, parallel corpora and labelled datasets around it, not anything intrinsic to the language itself.
- The Joshi et al. (ACL 2020) taxonomy has six classes: seven languages sit in Class 5 and 2,191 languages, about 88% of those studied, sit in Class 0 with almost no digital resources.
- Low-resource does not mean minor: Bhojpuri, Javanese, Hausa and Amharic each have tens of millions of speakers and sit well down the scale.
- The MMLU-ProX benchmark shows performance gaps of up to 24.3 points between high- and low-resource languages on identical questions across 36 models.
- Labelled data produced by native speakers is what moves a language up a class, and it is the one input that web scraping cannot supply.
What is the difference between a high-resource and a low-resource language?
A high-resource language has a large accumulated stock of digitised text, audio, dictionaries, parallel corpora and labelled datasets that can be used to train AI systems, while a low-resource language has very little of that stock. The difference is an economic and historical fact about a language's environment, not a property of the language.
Researchers at Microsoft Research India opened their 2020 paper with a puzzle. Two languages, each the official language of a country, with comparable native-speaker populations of around 29 million and around 18 million. One has roughly 2 million Wikipedia articles; the other about 5,500. One appears in 69 items across the two major linguistic data catalogues, LDC and ELRA; the other in 2. One is served by some of the best machine translation systems available; the other by very few, and poorly. The languages were Dutch and Somali.
Nothing about Somali makes it harder to learn or less expressive. What separates the two is a century of publishing, institutional investment, internet infrastructure and research attention accumulating on one side and not the other. The correction worth making early is that low-resource does not mean minor. Bhojpuri, Javanese, Hausa and Amharic each have tens of millions of speakers and sit well down the scale.
The two categories differ on the same set of criteria, and the table below puts them side by side.
| Criterion | High-resource languages | Low-resource languages |
|---|---|---|
| Taxonomy position | Classes 4 and 5 in Joshi et al. (ACL 2020) | Classes 0 to 2, with Class 3 as the boundary case |
| Number of languages | 25 (7 in Class 5, 18 in Class 4) | 2,432 (2,191 in Class 0, 222 in Class 1, 19 in Class 2) |
| Unlabelled web text | Abundant; dominant online presence | Sparse to non-existent in Class 0 and 1 |
| Labelled datasets | Plentiful and growing with sustained investment | Small community-built sets at best; almost none in Class 1 and 0 |
| Research attention | Class 5 ranks within the top 2 to 3 at major NLP conferences | Class 0 averages rank 600 to 1,000 |
| Benchmark performance | Some models score over 75% on Western European languages in MMLU-ProX | The same models fall as low as 0.6% on certain African languages |
| Typological coverage | Structural features well represented in training data | 549 of 1,139 feature categories appear only here |
| Example languages | English, Spanish, German, Japanese, French, Russian, Dutch, Korean | Bhojpuri, Cherokee, Fijian, Zulu, Lao, Maltese, Amharic |
| What lifts the class | Data can usually be sourced or scraped | Data usually has to be produced by native speakers |
How are languages classified by resource level?
The common reference is the six-class taxonomy in Joshi, Santy, Budhiraja, Bali and Choudhury, "The State and Fate of Linguistic Diversity and Inclusion in the NLP World" (ACL 2020), which sorts the world's languages by how much labelled and unlabelled data exists for each. The classes are usually given memorable names.
| Class | Name | Languages | Examples | Data position |
|---|---|---|---|---|
| 5 | The Winners | 7 | English, Spanish, German, Japanese, French | Dominant online presence and sustained investment; first to benefit from every advance |
| 4 | The Underdogs | 18 | Russian, Vietnamese, Korean, Dutch | Plenty of unlabelled data, less labelled data, active research communities |
| 3 | The Rising Stars | 28 | Indonesian, Ukrainian, Hebrew, Afrikaans | Strong web presence, but underserved by labelled dataset collection |
| 2 | The Hopefuls | 19 | Zulu, Lao, Maltese, Irish | Small sets of labelled data, usually built by determined communities |
| 1 | The Scraping-Bys | 222 | Bhojpuri, Cherokee, Fijian, Greenlandic | Some unlabelled text, almost no labelled data |
| 0 | The Left-Behinds | 2,191 | The long tail | Essentially no digital resources at all |
The numbers underneath make the picture stark. Class 5's seven languages cover around 2.5 billion speakers; Class 0's 2,191 languages, about 88% of all those studied, cover around 1.2 billion. The bottom class holds the overwhelming majority of the world's languages and roughly 15% of its speakers, with close to nothing to train on.
Read the table downward and one boundary stands out. The distinction between Class 3 and Class 4 is not really web presence, because Class 3 languages often have thriving online cultures. It is labelled data. The same is true a rung lower: what separates a Hopeful from a Rising Star is whether anyone has systematically produced annotated, verified datasets in that language. That is the gap a multilingual data collection partner is hired to close.
Why does the gap widen instead of closing?
Left alone, the distribution concentrates, because almost every mechanism in the system rewards languages that already have data. Four reinforcing loops do the work.
Pretraining amplifies what already exists. Modern models learn largely from large unlabelled corpora. That was supposed to democratise things, and for the middle classes it partly has. But a language with almost no unlabelled text online gains almost nothing from a technique that consumes it. As the original researchers put it, unsupervised pretraining methods only make the poor poorer.
Research attention follows resources. Analysis of publications across the main computational linguistics conferences found Class 5 languages ranking consistently within the top two or three, while Class 0 languages averaged ranks between 600 and 1,000. Fewer papers means fewer benchmarks, fewer tools and fewer trained researchers, which means fewer papers.
Commercial incentives compound it. Investment flows toward markets that can pay, and those markets mostly speak Class 4 and Class 5 languages. The languages with the weakest business case are frequently the ones with the greatest need.
Speakers migrate to the served language. This is the subtlest loop and the most consequential. When technology works in one language and not another, people switch to the one that works. Every switch reduces the digital output of the smaller language, weakening its position further. The tools do not merely reflect language inequality; they accelerate it.
What do the measurements actually show?
Three independent bodies of evidence point the same way: models trained mostly on high-resource languages perform markedly worse on low-resource ones, and for many languages the size of the gap was unknown until someone built a benchmark.
The typological gap. Comparing the structural features found in Classes 0 to 2 against those in Classes 3 to 5, the Microsoft study identified 549 feature categories out of 1,139 that exist in the under-resourced group and do not appear in the well-resourced one. Models trained on the top classes have never encountered nearly half the structural variety of human language. The consequence is specific: Amharic, the second most spoken Semitic language after Arabic, has nine typological features in that ignored group, while Arabic has none. On a cross-lingual task, English into Arabic produced an error rate of 7.8; English into Amharic produced 60.71. The gap is not explained by difficulty but by what the model was never shown.
The benchmark gap on identical content. MMLU-ProX translates the same 11,829 questions into 29 typologically diverse languages, so a score difference between languages is a difference in the model rather than in the questions. Evaluating 36 state-of-the-art models, including reasoning-enhanced and multilingual-optimised ones, the authors report performance disparities of up to 24.3 points between high- and low-resource languages, with some models achieving over 75% on Western European languages while scoring as low as 0.6% on certain African languages. The work was published at EMNLP 2025, and the method is the same one described in how multilingual AI models are benchmarked.
The measurement gap itself. For many languages the size of the gap was unknown rather than small, because no benchmark existed. A December 2024 study created roughly one million human-translated words of new benchmark data across eight low-resource African languages, Amharic, Bambara, Igbo, Sepedi, Shona, Sesotho, Setswana and Tsonga, covering more than 160 million speakers, precisely because standard benchmarks did not exist there.
The practical measure that follows is simple:
Coverage gap = Score in your strongest language − Score in the target language
Compute that per language rather than reporting a multilingual average. An aggregate score is dominated by the high-resource languages in the set, and a model can post a strong mean while failing in the language a market actually speaks. Measure it on evaluation data authored by speakers rather than translated from English; the process for that is set out in how to build multilingual evaluation sets for LLMs.
One further finding matters for planning: fluency and accuracy degrade at different rates. Models learn the shape of a language from relatively little data, so output stays grammatical well past the point where its factual reliability has dropped, and a reviewer who does not speak the language sees fluent text and concludes it is fine.
Does the commercial case follow the data?
No, it runs the other way, which is what makes the gap expensive rather than merely unfair. The markets where models are least reliable are frequently the markets where the local language matters most commercially.
CSA Research's survey of 8,709 consumers across 29 countries found that 76% of online shoppers prefer to buy products with information in their own language, and 40% will never buy from websites in other languages.
A programme that answers weak model performance by defaulting those markets to English has chosen the option that performs worst commercially, while appearing on the dashboard as a quality-conscious decision.
What actually moves a language up a class?
Labelled data produced by native speakers moves a language up a class. That is the binding constraint for almost every language below Class 4, and it is the one thing scraping cannot supply.
Resource class responds to investment, and there is a recent demonstration. Meta's Omnilingual ASR, released in November 2025, brought speech recognition to more than 1,600 languages, including 500 low-resource languages never before transcribed by AI. To reach languages with little or no digital presence, Meta worked with local organisations that recruited and compensated native speakers, often in remote or under-documented regions, alongside public datasets and community partnerships. Those languages did not rise because the internet changed. They rose because someone paid to go and collect the data. The recording and transcription pipeline behind that kind of work is described in how speech data is collected for low-resource languages.
Research on the classification found the same pattern from the other direction: some of the most neglected languages have small, focused communities working hard on them, while others with millions of speakers, Javanese and Igbo among them, have almost no such support.
Two planning conclusions follow, alongside the per-language coverage gap measurement.
- Check the class before you promise the market. Speaker count tells you the size of the opportunity; resource class tells you how much work reaching it takes. Confusing the two is how launch dates slip.
- Budget for data creation, not data acquisition. For Class 4 and 5 languages, data can often be sourced. Below that, it usually has to be produced, which is a different activity with different timelines and costs. How a multilingual corpus is specified and verified once it is produced is covered in multilingual LLM training data quality.
How does Lifewood approach low-resource languages?
Lifewood's multilingual work is built for the constraint this guide describes: producing labelled, in-language data where the web has not supplied it.
That means 100+ languages including underrepresented dialects, collected and verified through 40+ delivery centres across 30+ countries, with native speakers screened before a project begins and human review layered over automated checks at a 95%+ accuracy threshold. The network is distributed because moving a language up a class means having people in the places where it is spoken.
The service is delivered as managed multilingual data collection for text and conversational data, and as low-resource speech data collection for languages that have never had a transcribed corpus. Buyers comparing providers for this kind of work can start with the top multilingual AI data collection companies in 2026.