task_categories:question-answering task_ids:multiple-choice-qa annotations_creators:no-annotation language_creators:expert-generated multilinguality:monolingual source_datasets:original language:en license:mit size_categories:10K<n<100K arxiv:2312.09993 arxiv:2009.03300 region:us
---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: mmlu
pretty_name: Measuring Massive Multitask Language Understanding
language_bcp47:
- en-US
dataset_info:
- config_name: abstract_algebra
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- config_name: anatomy
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- config_name: astronomy
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- config_name: business_ethics
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- config_name: clinical_knowledge
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- config_name: college_biology
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- config_name: college_chemistry
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- config_name: college_computer_science
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- config_name: college_mathematics
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- config_name: college_medicine
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- config_name: college_physics
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- config_name: computer_security
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- config_name: conceptual_physics
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- config_name: econometrics
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- config_name: electrical_engineering
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- config_name: elementary_mathematics
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- config_name: formal_logic
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- config_name: global_facts
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- config_name: high_school_biology
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- config_name: high_school_chemistry
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- config_name: high_school_computer_science
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- config_name: high_school_european_history
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- config_name: high_school_geography
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- config_name: high_school_government_and_politics
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- config_name: high_school_macroeconomics
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- config_name: high_school_mathematics
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- config_name: high_school_microeconomics
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- config_name: high_school_physics
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- config_name: high_school_psychology
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- config_name: high_school_statistics
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- config_name: high_school_us_history
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- config_name: high_school_world_history
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- config_name: human_aging
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- config_name: human_sexuality
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- config_name: international_law
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- config_name: jurisprudence
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---

# Italian Version of the MMLU DATASET

Based on the version released by: [**FreedomIntelligence/MMLU_Italian**](https://huggingface.co/datasets/FreedomIntelligence/MMLU_Italian)
Includes minor fixes.


# Citations
This version:
```
@misc{basile2023llamantino,
      title={LLaMAntino: LLaMA 2 Models for Effective Text Generation in Italian Language}, 
      author={Pierpaolo Basile and Elio Musacchio and Marco Polignano and Lucia Siciliani and Giuseppe Fiameni and Giovanni Semeraro},
      year={2023},
      eprint={2312.09993},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```

Original Dataset:
```
    @article{hendryckstest2021,
      title={Measuring Massive Multitask Language Understanding},
      author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt},
      journal={Proceedings of the International Conference on Learning Representations (ICLR)},
      year={2021}
    }

    @article{hendrycks2021ethics,
      title={Aligning AI With Shared Human Values},
      author={Dan Hendrycks and Collin Burns and Steven Basart and Andrew Critch and Jerry Li and Dawn Song and Jacob Steinhardt},
      journal={Proceedings of the International Conference on Learning Representations (ICLR)},
      year={2021}
    }
```


# Original Dataset Card for MMLU

## Dataset Description

- **Repository**: https://github.com/hendrycks/test
- **Paper**: https://arxiv.org/abs/2009.03300

### Dataset Summary

[Measuring Massive Multitask Language Understanding](https://arxiv.org/pdf/2009.03300) by [Dan Hendrycks](https://people.eecs.berkeley.edu/~hendrycks/), [Collin Burns](http://collinpburns.com), [Steven Basart](https://stevenbas.art), Andy Zou, Mantas Mazeika, [Dawn Song](https://people.eecs.berkeley.edu/~dawnsong/), and [Jacob Steinhardt](https://www.stat.berkeley.edu/~jsteinhardt/) (ICLR 2021).

This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge. The test spans subjects in the humanities, social sciences, hard sciences, and other areas that are important for some people to learn. This covers 57 tasks including elementary mathematics, US history, computer science, law, and more. To attain high accuracy on this test, models must possess extensive world knowledge and problem solving ability.
A complete list of tasks: ['abstract_algebra', 'anatomy', 'astronomy', 'business_ethics', 'clinical_knowledge', 'college_biology', 'college_chemistry', 'college_computer_science', 'college_mathematics', 'college_medicine', 'college_physics', 'computer_security', 'conceptual_physics', 'econometrics', 'electrical_engineering', 'elementary_mathematics', 'formal_logic', 'global_facts', 'high_school_biology', 'high_school_chemistry', 'high_school_computer_science', 'high_school_european_history', 'high_school_geography', 'high_school_government_and_politics', 'high_school_macroeconomics', 'high_school_mathematics', 'high_school_microeconomics', 'high_school_physics', 'high_school_psychology', 'high_school_statistics', 'high_school_us_history', 'high_school_world_history', 'human_aging', 'human_sexuality', 'international_law', 'jurisprudence', 'logical_fallacies', 'machine_learning', 'management', 'marketing', 'medical_genetics', 'miscellaneous', 'moral_disputes', 'moral_scenarios', 'nutrition', 'philosophy', 'prehistory', 'professional_accounting', 'professional_law', 'professional_medicine', 'professional_psychology', 'public_relations', 'security_studies', 'sociology', 'us_foreign_policy', 'virology', 'world_religions']

### Languages

English

## Dataset Structure

### Data Instances

An example from anatomy subtask looks as follows:
```
{
  "question": "What is the embryological origin of the hyoid bone?",
  "choices": ["The first pharyngeal arch", "The first and second pharyngeal arches", "The second pharyngeal arch", "The second and third pharyngeal arches"],
  "answer": "D"
}
```

### Data Fields

- `question`: a string feature
- `choices`: a list of 4 string features
- `answer`: a ClassLabel feature

### Data Splits

- `auxiliary_train`: auxiliary multiple-choice training questions from ARC, MC_TEST, OBQA, RACE, etc.
- `dev`: 5 examples per subtask, meant for few-shot setting
- `test`: there are at least 100 examples per subtask

|       | auxiliary_train   | dev | val | test |
| ----- | :------: | :-----: | :-----: | :-----: |
| TOTAL | 99842 | 285 | 1531 | 14042


### Licensing Information

[MIT License](https://github.com/hendrycks/test/blob/master/LICENSE)


```
### Contributions

Thanks to [@andyzoujm](https://github.com/andyzoujm) for adding this dataset.
