README.md

---

pretty_name: SEA Natural Language Inference
license:
- cc-by-sa-4.0
- cc-by-nc-4.0
task_categories:
- text-generation
language:
- id
- ta
- th
- vi
dataset_info:
  features:
  - name: label
    dtype: string
  - name: prompts
    list:
    - name: sentence1
      dtype: string
    - name: sentence2
      dtype: string
  - name: prompt_templates
    sequence: string
  - name: metadata
    struct:
    - name: language
      dtype: string
  - name: id
    dtype: string
  splits:
  - name: id
    num_bytes: 829632
    num_examples: 1000
    num_tokens_gpt_4o: 48864
    num_tokens_gemma_2: 46813
    num_tokens_llama_3: 61750
  - name: id_fewshot
    num_bytes: 1026
    num_examples: 5
    num_tokens_gpt_4o: 209
    num_tokens_gemma_2: 191
    num_tokens_llama_3: 261
  - name: ta
    num_bytes: 1999488
    num_examples: 1000
    num_tokens_gpt_4o: 61925
    num_tokens_gemma_2: 83420
    num_tokens_llama_3: 245601
  - name: ta_fewshot
    num_bytes: 3228
    num_examples: 5
    num_tokens_gpt_4o: 365
    num_tokens_gemma_2: 507
    num_tokens_llama_3: 1495
  - name: th
    num_bytes: 1640723
    num_examples: 1000
    num_tokens_gpt_4o: 61000
    num_tokens_gemma_2: 57695
    num_tokens_llama_3: 71124
  - name: th_fewshot
    num_bytes: 2301
    num_examples: 5
    num_tokens_gpt_4o: 325
    num_tokens_gemma_2: 321
    num_tokens_llama_3: 362
  - name: vi
    num_bytes: 877251
    num_examples: 1000
    num_tokens_gpt_4o: 49181
    num_tokens_gemma_2: 47982
    num_tokens_llama_3: 48960
  - name: vi_fewshot
    num_bytes: 1245
    num_examples: 5
    num_tokens_gpt_4o: 260
    num_tokens_gemma_2: 257
    num_tokens_llama_3: 258
  download_size: 475196
  dataset_size: 5354894
  total_tokens_gpt_4o: 222129
  total_tokens_gemma_2: 237186
  total_tokens_llama_3: 429811
configs:
- config_name: default
  data_files:
  - split: id
    path: data/id-*
  - split: id_fewshot
    path: data/id_fewshot-*
  - split: ta
    path: data/ta-*
  - split: ta_fewshot
    path: data/ta_fewshot-*
  - split: th
    path: data/th-*
  - split: th_fewshot
    path: data/th_fewshot-*
  - split: vi
    path: data/vi-*
  - split: vi_fewshot
    path: data/vi_fewshot-*
size_categories:
- 1K<n<10K
---


# SEA Abstractive Summarization

SEA Abstractive Summarization evaluates a model's ability to read a document, identify the key points within, and summarize them into a coherent and fluent text while paraphrasing the document. It is sampled from [IndoNLI](https://aclanthology.org/2021.emnlp-main.821) for Indonesian, [IndicXNLI](https://aclanthology.org/2022.emnlp-main.755/) for Tamil, and [XNLI](https://aclanthology.org/D18-1269/) for Thai and Vietnamese.

### Supported Tasks and Leaderboards

SEA Abstractive Summarization 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)
- Tamil (ta)
- Thai (th)
- Vietnamese (vi)

### Dataset Details
SEA Abstractive Summarization 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 | 1000 | 48864 | 46813 | 61750
| ta | 1000 | 61925 | 83420 | 245601
| th | 1000 | 61000 | 57695 | 71124
| vi | 1000 | 49181 | 47982 | 48960
| id_fewshot | 5 | 209 | 191 | 261
| ta_fewshot | 5 | 365 | 507 | 1495
| th_fewshot | 5 | 325 | 321 | 362
| vi_fewshot | 5 | 260 | 257 | 258
| **total** | 4020 | 222129 | 237186 | 429811 |

### Data Sources

| Data Source | License | Language/s | Split/s
|-|:-|:-| :-|
| [IndoNLI](https://huggingface.co/datasets/afaji/indonli) | [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) | Indonesian | id, id_fewshot
| [IndicXNLI](https://huggingface.co/datasets/Divyanshu/indicxnli) | [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) | Tamil | ta, ta_fewshot
| [XNLI](https://huggingface.co/datasets/facebook/xnli) | [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) | Thai, Vietnamese | th, th_fewshot, vi, vi_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{mahendra-etal-2021-indonli,
      title = "{I}ndo{NLI}: A Natural Language Inference Dataset for {I}ndonesian",
      author = "Mahendra, Rahmad and Aji, Alham Fikri and Louvan, Samuel and Rahman, Fahrurrozi and Vania, Clara",
      booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
      month = nov,
      year = "2021",
      address = "Online and Punta Cana, Dominican Republic",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/2021.emnlp-main.821",
      pages = "10511--10527",
}

@misc{aggarwal2022indicxnlievaluatingmultilingualinference,
      title={IndicXNLI: Evaluating Multilingual Inference for Indian Languages}, 
      author={Divyanshu Aggarwal and Vivek Gupta and Anoop Kunchukuttan},
      year={2022},
      eprint={2204.08776},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2204.08776}, 
}

@InProceedings{conneau2018xnli,
      author = {Conneau, Alexis and Rinott, Ruty and Lample, Guillaume and Williams, Adina and Bowman, Samuel R. and Schwenk, Holger and Stoyanov, Veselin},
      title = {XNLI: Evaluating Cross-lingual Sentence Representations},
      booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
      year = {2018},
      publisher = {Association for Computational Linguistics},
      location = {Brussels, Belgium},
}

@misc{leong2023bhasaholisticsoutheastasian,
      title={BHASA: A Holistic Southeast Asian Linguistic and Cultural Evaluation Suite for Large Language Models}, 
      author={Wei Qi Leong and Jian Gang Ngui and Yosephine Susanto and Hamsawardhini Rengarajan and Kengatharaiyer Sarveswaran and William Chandra Tjhi},
      year={2023},
      eprint={2309.06085},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2309.06085}, 
}
```