developer0hye/llm-speed-prompt-mix

Tasks

• text-generation
• question-answering

Citation text from the card

Please cite the sources of the subsets you use:

- **MMLU**: Hendrycks et al., Measuring Massive Multitask Language Understanding, ICLR 2021. <https://arxiv.org/abs/2009.03300>
- **MMMLU**: OpenAI, Multilingual Massive Multitask Language Understanding. <https://huggingface.co/datasets/openai/MMMLU>
- **GSM8K**: Cobbe et al., Training Verifiers to Solve Math Word Problems, 2021. <https://arxiv.org/abs/2110.14168>
- **HRM8K**: Ko et al., Understand, Solve and Translate: Bridging the Multilingual Mathematical Reasoning Gap, 2025. <https://arxiv.org/abs/2501.02448>
- **MGSM**: Shi et al., Language Models are Multilingual Chain-of-Thought Reasoners, ICLR 2023. <https://arxiv.org/abs/2210.03057>
- **HumanEval**: Chen et al., Evaluating Large Language Models Trained on Code, 2021. <https://arxiv.org/abs/2107.03374>
- **MBPP**: Austin et al., Program Synthesis with Large Language Models, 2021. <https://arxiv.org/abs/2108.07732>
- **HumanEval-X**: Zheng et al., CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X, KDD 2023. <https://arxiv.org/abs/2303.17568>
- **Belebele**: Bandarkar et al., The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants, ACL 2024. <https://arxiv.org/abs/2308.16884>
- **FLORES-200**: NLLB Team, No Language Left Behind: Scaling Human-Centered Machine Translation, 2022. <https://arxiv.org/abs/2207.04672>
- **MT-Bench**: Zheng et al., Judging LLM-as-a-Judge with MT-Bench and Chatbot Are

All datasets marked Permissive or share-alike | JSON