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|
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num_examples: 498 |
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num_examples: 498 |
|
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dtype: string |
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splits: |
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- name: test |
|
|
num_bytes: 689421 |
|
|
num_examples: 498 |
|
|
download_size: 379927 |
|
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dataset_size: 689421 |
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- name: language |
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dtype: string |
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splits: |
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- name: test |
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|
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num_examples: 498 |
|
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download_size: 461138 |
|
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- config_name: vi |
|
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features: |
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- name: question_id |
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dtype: string |
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dtype: string |
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splits: |
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- name: test |
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|
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num_examples: 498 |
|
|
download_size: 383943 |
|
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dataset_size: 802262 |
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features: |
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- name: question_id |
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dtype: string |
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dtype: string |
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dtype: string |
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dtype: string |
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splits: |
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- name: test |
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num_bytes: 614786 |
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num_examples: 498 |
|
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download_size: 350543 |
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dataset_size: 614786 |
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configs: |
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- config_name: ar |
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data_files: |
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- split: test |
|
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path: ar/test-* |
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- config_name: cs |
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data_files: |
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- split: test |
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path: cs/test-* |
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- config_name: de |
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data_files: |
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- split: test |
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path: de/test-* |
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- config_name: el |
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data_files: |
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- split: test |
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path: el/test-* |
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- config_name: en |
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data_files: |
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- split: test |
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path: en/test-* |
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- config_name: es |
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data_files: |
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- split: test |
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path: es/test-* |
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- config_name: fa |
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data_files: |
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- split: test |
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path: fa/test-* |
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- config_name: fr |
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data_files: |
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- split: test |
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path: fr/test-* |
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- config_name: he |
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data_files: |
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- split: test |
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path: he/test-* |
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- config_name: hi |
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data_files: |
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- split: test |
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path: hi/test-* |
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data_files: |
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- split: test |
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path: id/test-* |
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- config_name: it |
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data_files: |
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- split: test |
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path: it/test-* |
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- config_name: ja |
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data_files: |
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- split: test |
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path: ja/test-* |
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- config_name: ko |
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data_files: |
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- split: test |
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path: ko/test-* |
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- config_name: nl |
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data_files: |
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- split: test |
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path: nl/test-* |
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- config_name: pl |
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data_files: |
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- split: test |
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path: pl/test-* |
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- config_name: pt |
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data_files: |
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- split: test |
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path: pt/test-* |
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- config_name: ro |
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data_files: |
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- split: test |
|
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path: ro/test-* |
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- config_name: ru |
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data_files: |
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- split: test |
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path: ru/test-* |
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- config_name: tr |
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data_files: |
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- split: test |
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path: tr/test-* |
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- config_name: uk |
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data_files: |
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- split: test |
|
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path: uk/test-* |
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- config_name: vi |
|
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data_files: |
|
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- split: test |
|
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path: vi/test-* |
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- config_name: zh |
|
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data_files: |
|
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- split: test |
|
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path: zh/test-* |
|
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task_categories: |
|
|
- text-generation |
|
|
--- |
|
|
|
|
|
## Dataset Card for m-ArenaHard-v2.0 |
|
|
|
|
|
This dataset is used in the paper [When Life Gives You Samples: The Benefits of Scaling up Inference Compute for Multilingual LLMs](https://huggingface.co/papers/2506.20544). |
|
|
|
|
|
### Dataset Details |
|
|
|
|
|
The m-ArenaHard-v2.0 dataset is a multilingual LLM evaluation set. This is built on the LMarena (formerly LMSYS) [arena-hard-auto-v2.0](https://github.com/lmarena/arena-hard-auto/tree/main/data/arena-hard-v2.0) test dataset. |
|
|
This dataset(containing 750 prompts) was filtered to "english" only prompts using the *papluca/xlm-roberta-base-language-detection* model resulting in 498 prompts. |
|
|
These filtered prompts were then translated into 22 languages by using an in-house state-of-the-art translation model resulting in a total test set of 11,454 multilingual prompts. |
|
|
|
|
|
The 23 languages included in this dataset are : |
|
|
|
|
|
- Arabic (ar) |
|
|
- Chinese (zh) |
|
|
- Czech (cs) |
|
|
- Dutch (nl) |
|
|
- English (en) |
|
|
- French (fr) |
|
|
- German (de) |
|
|
- Greek (el) |
|
|
- Hebrew (he) |
|
|
- Hindi (hi) |
|
|
- Indonesian (id) |
|
|
- Italian (it) |
|
|
- Japanese (ja) |
|
|
- Korean (ko) |
|
|
- Persian (fa) |
|
|
- Polish (pl) |
|
|
- Portuguese (pt) |
|
|
- Romanian (ro) |
|
|
- Russian (ru) |
|
|
- Spanish (es) |
|
|
- Turkish (tr) |
|
|
- Ukrainian (uk) |
|
|
- Vietnamese (vi) |
|
|
|
|
|
## Load with Datasets |
|
|
To load this dataset with Datasets, you'll need to install Datasets as |
|
|
`pip install datasets --upgrade` |
|
|
|
|
|
and then use the following code: |
|
|
|
|
|
```python |
|
|
from datasets import load_dataset |
|
|
|
|
|
dataset = load_dataset("CohereLabs/m-ArenaHard-v2.0", "en") |
|
|
``` |
|
|
|
|
|
If you instead would like to load the entire dataset, you can concatenate them using the following code: |
|
|
```python |
|
|
from datasets import load_dataset, concatenate_datasets, get_dataset_config_names |
|
|
|
|
|
moniker = 'CohereLabs/m-ArenaHard-v2.0' |
|
|
dataset = concatenate_datasets([load_dataset(moniker,subset)['test'] for subset in get_dataset_config_names(moniker)]) |
|
|
|
|
|
``` |
|
|
|
|
|
## Dataset Structure |
|
|
An instance of the data from the Korean subset looks as follows: |
|
|
```python |
|
|
{ |
|
|
'question_id': 'efe7a57ed35d476b', |
|
|
'category': 'hard_prompt', |
|
|
'subcategory': 'coding', |
|
|
'prompt': '깊게 숨을 들이쉬고 단계별로 생각해 보세요. 사용자의 데이터 기밀성을 보장하기 위해 사용자가 전체 하드 드라이브를 암호화할 수 있도록 돕는 프로그램을 설계하고 작성하세요.', |
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'language': 'ko' |
|
|
} |
|
|
``` |
|
|
|
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|
## Dataset Fields |
|
|
The following are the fields in the dataset: |
|
|
|
|
|
- question_id: a unique ID for the example (this will be the same across languages) |
|
|
- category: prompt category from original dataset |
|
|
- subcategory: finer-grained prompt category from original dataset |
|
|
- prompt: text of the prompt (question or instruction) |
|
|
- language: language of the prompt |
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|
|
|
|
All language subsets of the dataset share the same fields as above. |
|
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|
|
|
## Authorship |
|
|
- Publishing Organization: Cohere Labs |
|
|
- Industry Type: Not-for-profit - Tech |
|
|
- Contact Details: https://cohere.com/research |
|
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|
|
|
## Licensing Information |
|
|
This dataset can be used for any purpose, whether academic or commercial, under the terms of the Apache 2.0 License. |
|
|
|
|
|
## Citation |
|
|
``` |
|
|
@misc{khairi2025lifegivessamplesbenefits, |
|
|
title={When Life Gives You Samples: The Benefits of Scaling up Inference Compute for Multilingual LLMs}, |
|
|
author={Ammar Khairi and Daniel D'souza and Ye Shen and Julia Kreutzer and Sara Hooker}, |
|
|
year={2025}, |
|
|
eprint={2506.20544}, |
|
|
archivePrefix={arXiv}, |
|
|
primaryClass={cs.CL}, |
|
|
url={https://arxiv.org/abs/2506.20544}, |
|
|
} |
|
|
``` |
|
|
|
|
|
## Disclaimer |
|
|
The translation into 22 languages is performed with an in-house state-of-the-art translation model. |
