metadata

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
    - name: id_fewshot
      num_bytes: 1026
      num_examples: 5
    - name: ta
      num_bytes: 1999488
      num_examples: 1000
    - name: ta_fewshot
      num_bytes: 3228
      num_examples: 5
    - name: th
      num_bytes: 1640723
      num_examples: 1000
    - name: th_fewshot
      num_bytes: 2301
      num_examples: 5
    - name: vi
      num_bytes: 877251
      num_examples: 1000
    - name: vi_fewshot
      num_bytes: 1245
      num_examples: 5
  download_size: 475196
  dataset_size: 5354894
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 for Indonesian, IndicXNLI for Tamil, and XNLI 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 leaderboard from AI Singapore.

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	CC BY-SA 4.0	Indonesian	id, id_fewshot
IndicXNLI	CC BY-NC 4.0	Tamil	ta, ta_fewshot
XNLI	CC 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

@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}, 
}