README.md

metadata

pretty_name: SEA Question Answering
license:
  - apache-2.0
  - cc-by-4.0
  - cc-by-sa-4.0
task_categories:
  - text-generation
  - question-answering
language:
  - id
  - ta
  - th
  - vi
dataset_info:
  features:
    - name: id
      dtype: string
    - name: label
      dtype: string
    - name: prompts
      list:
        - name: question
          dtype: string
        - name: text
          dtype: string
    - name: prompt_templates
      sequence: string
    - name: metadata
      struct:
        - name: language
          dtype: string
  splits:
    - name: id
      num_bytes: 106759
      num_examples: 100
    - name: id_fewshot
      num_bytes: 1588
      num_examples: 5
    - name: ta
      num_bytes: 709785
      num_examples: 100
    - name: ta_fewshot
      num_bytes: 18675
      num_examples: 5
    - name: th
      num_bytes: 294955
      num_examples: 100
    - name: th_fewshot
      num_bytes: 5742
      num_examples: 5
    - name: vi
      num_bytes: 158410
      num_examples: 100
    - name: vi_fewshot
      num_bytes: 2927
      num_examples: 5
  download_size: 459628
  dataset_size: 1298841
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 Question Answering

SEA Question Answering evaluates a model's ability to predict a contiguous span of characters that answers the question about a given passage. It is sampled from TyDi QA-GoldP for Indonesian, IndicQA for Tamil, and XQuaD for Thai and Vietnamese.

Supported Tasks and Leaderboards

SEA Question Answering 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 Question Answering 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	100	16000	15099	19380
ta	100	709785	83356	110080
th	100	33266	33052	37164
vi	100	25064	24086	23722
id_fewshot	5	372	375	466
ta_fewshot	5	2459	3260	9165
th_fewshot	5	781	885	926
vi_fewshot	5	574	550	548
total	420	161872	187387	405552

Data Sources

Data Source	License	Language/s	Split/s
TyDi QA-GoldP	Apache 2.0	Indonesian	id, id_fewshot
IndicQA	CC BY 4.0	Tamil	ta, ta_fewshot
XQUAD	CC BY-SA 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

@article{tydiqa,
      title   = {TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
      author  = {Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki}
      year    = {2020},
      journal = {Transactions of the Association for Computational Linguistics}
}

@inproceedings{doddapaneni-etal-2023-towards,
    title = "Towards Leaving No {I}ndic Language Behind: Building Monolingual Corpora, Benchmark and Models for {I}ndic Languages",
    author = "Doddapaneni, Sumanth  and
      Aralikatte, Rahul  and
      Ramesh, Gowtham  and
      Goyal, Shreya  and
      Khapra, Mitesh M.  and
      Kunchukuttan, Anoop  and
      Kumar, Pratyush",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.693",
    doi = "10.18653/v1/2023.acl-long.693",
    pages = "12402--12426",
}

@inproceedings{artetxe-etal-2020-cross,
    title = "On the Cross-lingual Transferability of Monolingual Representations",
    author = "Artetxe, Mikel  and
      Ruder, Sebastian  and
      Yogatama, Dani",
    editor = "Jurafsky, Dan  and
      Chai, Joyce  and
      Schluter, Natalie  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.acl-main.421",
    doi = "10.18653/v1/2020.acl-main.421",
    pages = "4623--4637",
}

@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}, 
}