---
license: apache-2.0
configs:
- config_name: '2022'
  data_files: 2022.jsonl
- config_name: '2023'
  data_files: 2023.jsonl
- config_name: '2024'
  data_files: 2024.jsonl
  default: true
dataset_info:
  features:
  - name: id
    dtype: string
  - name: exam
    dtype: string
  - name: IU
    dtype: bool
  - name: ledor
    dtype: bool
  - name: question
    dtype: string
  - name: alternatives
    sequence: string
  - name: figures
    sequence: string
  - name: description
    sequence: string
  - name: label
    dtype: string
task_categories:
- visual-question-answering
- multiple-choice
language:
- pt
pretty_name: ENEM
size_categories:
- n<1K
---

The enem 2022 and enem 2023 datasets encompass all multiple-choice questions from the last two editions of the [Exame Nacional do Ensino Médio (ENEM)](https://www.gov.br/inep/pt-br/areas-de-atuacao/avaliacao-e-exames-educacionais/enem), the main standardized entrance examination adopted by Brazilian universities. The datasets have been created to allow the evaluation of both textual-only and textual-visual language models. To evaluate textual-only models, we incorporated into the datasets the textual descriptions of the images that appear in the questions' statements from the orange ENEM exam booklet, a particular booklet that offers accessibility to people with visual impairments.

A repository containing the essential code for utilizing this dataset is accessible [here](https://github.com/piresramon/gpt-4-enem).

If you use this dataset in your research, please acknowledge the papers below by citing them:

```bibtex
@misc{pires2023evaluating,
      title={Evaluating GPT-4's Vision Capabilities on Brazilian University Admission Exams}, 
      author={Ramon Pires and Thales Sales Almeida and Hugo Abonizio and Rodrigo Nogueira},
      year={2023},
      eprint={2311.14169},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```

```bibtex
@misc{nunes2023evaluating,
      title={Evaluating GPT-3.5 and GPT-4 Models on Brazilian University Admission Exams}, 
      author={Desnes Nunes and Ricardo Primi and Ramon Pires and Roberto Lotufo and Rodrigo Nogueira},
      year={2023},
      eprint={2303.17003},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```