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Datasets:
LLaMAX
/
BenchMAX_Model-based

Tasks:
Text Generation
Modalities:
Text
Formats:
json
Languages:
English
Chinese
Spanish
Size:
1K - 10K
ArXiv:
Tags:
multilingual
instruction-following
Libraries:
Datasets
pandas
License:
Dataset card Data Studio Files Community
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metadata
license: cc-by-4.0
task_categories:
  - text-generation
language:
  - en
  - zh
  - es
  - fr
  - de
  - ru
  - ja
  - th
  - sw
  - te
  - bn
  - ar
  - ko
  - vi
  - cs
  - hu
  - sr
multilinguality:
  - multilingual
size_categories:
  - 1K<n<10K
configs:
  - config_name: en
    data_files: arenahard_en.jsonl
  - config_name: zh
    data_files: arenahard_zh.jsonl
  - config_name: es
    data_files: arenahard_es.jsonl
  - config_name: fr
    data_files: arenahard_fr.jsonl
  - config_name: de
    data_files: arenahard_de.jsonl
  - config_name: ru
    data_files: arenahard_ru.jsonl
  - config_name: ja
    data_files: arenahard_ja.jsonl
  - config_name: th
    data_files: arenahard_th.jsonl
  - config_name: bn
    data_files: arenahard_bn.jsonl
  - config_name: sw
    data_files: arenahard_sw.jsonl
  - config_name: te
    data_files: arenahard_te.jsonl
  - config_name: ar
    data_files: arenahard_ar.jsonl
  - config_name: ko
    data_files: arenahard_ko.jsonl
  - config_name: vi
    data_files: arenahard_vi.jsonl
  - config_name: cs
    data_files: arenahard_cs.jsonl
  - config_name: hu
    data_files: arenahard_hu.jsonl
  - config_name: sr
    data_files: arenahard_sr.jsonl
tags:
  - multilingual
  - instruction-following

Dataset Sources

Dataset Description

BenchMAX_Model-based is a dataset of BenchMAX, sourcing from m-ArenaHard, which evaluates the instruction following capability via model-based judgment.

We extend the original dataset to include languages that are not supported by m-ArenaHard through Google Translate. Then manual post-editing is applied for all non-English languages.

Usage 

git clone https://github.com/CONE-MT/BenchMAX.git
cd BenchMAX
pip install -r requirements.txt

cd tasks/arenahard
bash prepare.sh

Then modify the model configs in arena-hard-auto/config. Please add your model config to api_config.yaml and add your model name to the model list in other configs like gen_answer_config_*.yaml. If you want to change the judge model, you can modify judge_config_*.yaml.

Finally, deploy your model and run the evaluation, where your model first generates responses to prompts and DeepSeek-V3 judge them against GPT-4o responses, as we do in the paper.

# serve your model by vllm
vllm serve meta-llama/Llama-3.1-8B-Instruct

# generate responses
cd arena-hard-auto
languages=(en ar bn cs de es fr hu ja ko ru sr sw te th vi zh)
for lang in "${languages[@]}"; do
    python gen_answer.py --setting-file config/gen_answer_config_${lang}.yaml
done

# run LLM-as-a-judge
export OPENAI_API_KEY=...
for lang in "${languages[@]}"; do
    python gen_judgment.py --setting-file config/judge_config_${lang}.yaml
done

Supported Languages

Arabic, Bengali, Chinese, Czech, English, French, German, Hungarian, Japanese, Korean, Serbian, Spanish, Swahili, Telugu, Thai, Russian, Vietnamese

Citation

If you find our dataset helpful, please cite this paper:

@article{huang2025benchmax,
  title={BenchMAX: A Comprehensive Multilingual Evaluation Suite for Large Language Models},
  author={Huang, Xu and Zhu, Wenhao and Hu, Hanxu and He, Conghui and Li, Lei and Huang, Shujian and Yuan, Fei},
  journal={arXiv preprint arXiv:2502.07346},
  year={2025}
}