aisingapore/Safety-Toxicity-Detection

SEA Toxicity Detection

SEA Toxicity Detection evaluates a model's ability to identify toxic content such as hate speech and abusive language in text. It is sampled from MLHSD for Indonesian, TTD for Thai, and ViHSD for Vietnamese.

Supported Tasks and Leaderboards

SEA Toxicity Detection 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)
• Thai (th)
• Vietnamese (vi)

Dataset Details

SEA Toxicity Detection 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	34416	34238	40537
th	1000	38189	35980	42901
vi	1000	17540	16904	18287
id_fewshot	5	183	174	216
th_fewshot	5	130	121	150
vi_fewshot	5	104	97	104
total	3015	90562	87514	102195

Data Sources

Data Source	License	Language/s	Split/s
MLHSD	CC BY-NC-SA 4.0	Indonesian	id, id_fewshot
TTD	CC BY-NC 3.0	Thai	th, th_fewshot
ViHSD	CC BY-NC 4.0	Vietnamese	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.

Acknowledgement

This project is supported by the National Research Foundation Singapore and Infocomm Media Development Authority (IMDA), Singapore under its National Large Language Model Funding Initiative.

References

@inproceedings{ibrohim-budi-2019-multi,
    title = "Multi-label Hate Speech and Abusive Language Detection in {I}ndonesian {T}witter",
    author = "Ibrohim, Muhammad Okky  and
      Budi, Indra",
    editor = "Roberts, Sarah T.  and
      Tetreault, Joel  and
      Prabhakaran, Vinodkumar  and
      Waseem, Zeerak",
    booktitle = "Proceedings of the Third Workshop on Abusive Language Online",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W19-3506",
    doi = "10.18653/v1/W19-3506",
    pages = "46--57",
}

@inproceedings{sirihattasak2018annotation,
  title={Annotation and classification of toxicity for Thai Twitter},
  author={Sirihattasak, Sugan and Komachi, Mamoru and Ishikawa, Hiroshi},
  booktitle={TA-COS 2018: 2nd Workshop on Text Analytics for Cybersecurity and Online Safety},
  pages={1},
  year={2018}
}

@InProceedings{10.1007/978-3-030-79457-6_35,
      author="Luu, Son T.
      and Nguyen, Kiet Van
      and Nguyen, Ngan Luu-Thuy",
      editor="Fujita, Hamido
      and Selamat, Ali
      and Lin, Jerry Chun-Wei
      and Ali, Moonis",
      title="A Large-Scale Dataset for Hate Speech Detection on Vietnamese Social Media Texts",
      booktitle="Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices",
      year="2021",
      publisher="Springer International Publishing",
      address="Cham",
      pages="415--426",
      isbn="978-3-030-79457-6"
}

@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}, 
}