---
pretty_name: SEA Metaphor
license:
- mit
task_categories:
- text-generation
- question-answering
language:
- id
- jv
- su
dataset_info:
  features:
  - name: id
    dtype: string
  - name: label
    dtype: string
  - name: prompts
    list:
    - name: phrase
      dtype: string
    - name: choice1
      dtype: string
    - name: choice2
      dtype: string
  - name: prompt_templates
    sequence: string
  - name: metadata
    struct:
    - name: language
      dtype: string
  splits:
  - name: id
    num_bytes: 163609
    num_examples: 295
  - name: id_fewshot
    num_bytes: 905
    num_examples: 5
  - name: jv
    num_bytes: 141631
    num_examples: 282
  - name: jv_fewshot
    num_bytes: 714
    num_examples: 5
  - name: su
    num_bytes: 139902
    num_examples: 295
  - name: su_fewshot
    num_bytes: 701
    num_examples: 5
  download_size: 85457
  dataset_size: 447462
configs:
- config_name: default
  data_files:
  - split: id
    path: data/id-*
  - split: id_fewshot
    path: data/id_fewshot-*
  - split: jv
    path: data/jv-*
  - split: jv_fewshot
    path: data/jv_fewshot-*
  - split: su
    path: data/su-*
  - split: su_fewshot
    path: data/su_fewshot-*
size_categories:
- 1K<n<10K
---

# SEA Metaphor

SEA Metaphor evaluates a model's ability to interpret paired figurative phrases with divergent meanings. It is sampled from [Multilingual-Fig-QA](https://aclanthology.org/2023.findings-acl.525) for Indonesian, Javanese, and Sundanese.

### Supported Tasks and Leaderboards

SEA Metaphor is designed for evaluating chat or instruction-tuned large language models (LLMs). It is part of the [SEA-HELM](https://leaderboard.sea-lion.ai/) leaderboard from [AI Singapore](https://aisingapore.org/).

### Languages
- Indonesian (id)
- Javanese (jv)
- Sundanese (su)

### Dataset Details
SEA Metaphor is split by language, with additional splits containing fewshot examples. Below are the statistics for this dataset. The number of tokens only refer to the strings of text found within the `prompts` column.

| Split | # of examples | # of GPT-4o tokens | # of Gemma 2 tokens | # of Llama 3 tokens |
|-|:-|:-|:-|:-|
| id | 295 | 10289 | 10120 | 13265
| jv | 282 | 10167 | 10923 | 13279
| su | 295 | 8973 | 10046 | 11858
| id_fewshot | 5 | 183 | 178 | 239
| jv_fewshot | 5 | 154 | 168 | 198
| su_fewshot | 5 | 153 | 165 | 193
| **total** | 887 | 29919 | 31600 | 39032 |

### Data Sources

| Data Source | License | Language/s | Split/s
|-|:-|:-| :-|
| [Multilingual-Fig-QA](https://github.com/simran-khanuja/Multilingual-Fig-QA) | [MIT](https://mit-license.org/) | Indonesian, Javanese, Sundanese | id, id_fewshot, jv, jv_fewshot, su, su_fewshot

### License

For the license/s of the dataset/s, please refer to the data sources table above.

We endeavor to ensure data used is permissible and have chosen datasets from creators who have processes to exclude copyrighted or disputed data. 


### References

```bibtex
@inproceedings{kabra-etal-2023-multi,
    title = "Multi-lingual and Multi-cultural Figurative Language Understanding",
    author = "Kabra, Anubha  and
      Liu, Emmy  and
      Khanuja, Simran  and
      Aji, Alham Fikri  and
      Winata, Genta  and
      Cahyawijaya, Samuel  and
      Aremu, Anuoluwapo  and
      Ogayo, Perez  and
      Neubig, Graham",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.525",
    doi = "10.18653/v1/2023.findings-acl.525",
    pages = "8269--8284",
}
```