Datasets:
Dataset Viewer
benchmarks
dict |
|---|
{
"country_metadata": {
"country": "Bangladesh",
"description": "Benchmark for Bangladesh Indigenous Languages",
"iso_3166_code": "bd",
"number_of_domain": 1,
"number_of_languages": 11,
"number_of_tasks": 2,
"source": "https://www.ethnologue.com/country/BD/"
},
"id": 1,
"results": [
{
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"source_languages": [
"French"
],
"type": "translation"
},
{
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"general"
],
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],
"name": "other",
"score": 0,
"source_languages": [
"French"
],
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}
],
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]
},
{
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{
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"source_languages": [
"French"
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},
{
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"general"
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],
"name": "other",
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],
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],
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]
},
{
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{
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],
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]
},
{
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{
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{
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]
},
{
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{
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{
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},
{
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{
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{
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},
{
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{
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{
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],
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]
},
{
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{
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{
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]
},
{
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{
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{
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]
},
{
"language": "Santhali",
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{
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],
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]
},
{
"language": "Sylheti",
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{
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],
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}
]
}
]
}
|
{
"country_metadata": {
"country": "Bolivia",
"description": "Benchmark for Bolivia Indigenous Languages",
"iso_3166_code": "bo",
"number_of_domain": 1,
"number_of_languages": 10,
"number_of_tasks": 2,
"source": "https://www.ethnologue.com/country/BO/"
},
"id": 2,
"results": [
{
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],
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{
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"name": "other",
"score": 0,
"source_languages": [
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],
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}
],
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}
]
},
{
"language": "Cavineña",
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{
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],
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]
},
{
"language": "Central Aymara",
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{
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{
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{
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},
{
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{
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{
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{
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{
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]
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]
}
|
{
"country_metadata": {
"country": "Brazil",
"description": "Benchmark for Brazil Indigenous Languages",
"iso_3166_code": "br",
"number_of_domain": 1,
"number_of_languages": 31,
"number_of_tasks": 2,
"source": "https://www.ethnologue.com/country/BR/"
},
"id": 3,
"results": [
{
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},
{
"language": "Enawené-Nawé",
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},
{
"language": "Hixkaryána",
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},
{
"language": "Hunsrik",
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{
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},
{
"language": "Ikpeng",
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{
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},
{
"language": "Ingarikó",
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{
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|
End of preview. Expand
in Data Studio
Dataset Details
Dataset Description
We evaluated 100+ Large Language Models (LLMs) to address a fundamental challenge:
the accurate assessment of AI linguistic capabilities on low-resource languages.
In the context of international development, where linguistic diversity is immense, it is crucial that AI systems can communicate effectively and fairly with all populations.
However, many languages still lack sufficient digital corpora for training, which often results in significant gaps in the models’ ability to reliably understand, translate, and generate text.
The purpose of this dataset is to provide a robust linguistic benchmarking methodology designed to:
measure LLM performance on underrepresented languages,
identify current limitations,
foster the development of more inclusive and equitable AI solutions.
Language(s) (NLP): Over 1000 low-resource languages, including Swahili, Kikuyu, Luhya, Luo, Kamba, Kisii, Meru, Maasai (Kenya), and …
License: agpl-3.0
Acknowledgment
We thank AFD - Agence Française de Développement for their generous sponsorship.
If you'd like to sponsor us, please get in touch.
Dataset Sources
- Repository: https://github.com/judeLuther/EvaluLLM
Dataset Structure
{ "benchmarks": [ { "id": "int", "country_metadata": { "country": "string", "iso_3166_code": "string", "source": "string (URL)", "description": "string" }, "results": [ { "language": "string", "models": [ { "model_name": "string", "max_tokens": "int", "embed_dim": "int", "n_parameters": "int", "zero_shot_percentage": "float", "confidence": "int", "text_length": "int", "reference": "string (URL or citation)", "task_type": [ { "name": "string", "type": "string", "score": "float (0.0 – 1.0)", "source_languages": ["string"], "domains": ["string"], "modalities": ["string"] } ] } ] } ] } ] }
Bias, Risks, and Limitations
This dataset was generated by evaluating LLMs with other LLMs, which means that no human evaluators were involved for all languages.
As a result, there may be limitations in the accuracy and reliability of the evaluations, especially for low-resource languages where the LLMs may have limited training data.
Potential risks and limitations include:
- Evaluation bias: The scores reflect the abilities of the evaluating LLMs, which may themselves have biases or gaps.
- Language coverage: Some languages may be underrepresented, and results should not be considered fully comprehensive.
- Sociolinguistic nuances: Cultural and contextual understanding might not be captured accurately by automated evaluation.
Users should interpret the results with caution and consider supplementing with human evaluation where possible.
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