UTibetNLP/cino-base-v2_TNCC-title_tsheg

Introduction

This is a fine-tuned LM in our papers below and the related GitHub repo is here.

Human-in-the-Loop Generation of Adversarial Texts: A Case Study on Tibetan Script (Cao et al., IJCNLP-AACL 2025 Demo)

TSCheater: Generating High-Quality Tibetan Adversarial Texts via Visual Similarity (Cao et al., ICASSP 2025)

Multi-Granularity Tibetan Textual Adversarial Attack Method Based on Masked Language Model (Cao et al., WWW 2024 Workshop - SocialNLP)

Pay Attention to the Robustness of Chinese Minority Language Models! Syllable-level Textual Adversarial Attack on Tibetan Script (Cao et al., ACL 2023 Workshop - TrustNLP)

Citation

If you think our work useful, please kindly cite our paper.

@inproceedings{cao-etal-2025-human,
    title = "Human-in-the-Loop Generation of Adversarial Texts: A Case Study on {T}ibetan Script",
    author = "Cao, Xi  and
      Sun, Yuan  and
      Li, Jiajun  and
      Gesang, Quzong  and
      Qun, Nuo  and
      Tashi, Nyima",
    editor = "Liu, Xuebo  and
      Purwarianti, Ayu",
    booktitle = "Proceedings of The 14th International Joint Conference on Natural Language Processing and The 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.ijcnlp-demo.2/",
    pages = "9--16",
    ISBN = "979-8-89176-301-2"
}

@INPROCEEDINGS{10889732,
  author={Cao, Xi and Gesang, Quzong and Sun, Yuan and Qun, Nuo and Nyima, Tashi},
  booktitle={ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
  title={TSCheater: Generating High-Quality Tibetan Adversarial Texts via Visual Similarity}, 
  year={2025},
  volume={},
  number={},
  pages={1-5},
  doi={10.1109/ICASSP49660.2025.10889732}}

@inproceedings{10.1145/3589335.3652503,
    author = {Cao, Xi and Qun, Nuo and Gesang, Quzong and Zhu, Yulei and Nyima, Trashi},
    title = {Multi-Granularity Tibetan Textual Adversarial Attack Method Based on Masked Language Model},
    year = {2024},
    isbn = {9798400701726},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3589335.3652503},
    doi = {10.1145/3589335.3652503},
    booktitle = {Companion Proceedings of the ACM on Web Conference 2024},
    pages = {1672–1680},
    numpages = {9},
    keywords = {language model, robustness, textual adversarial attack, tibetan},
    location = {Singapore, Singapore},
    series = {WWW '24}
}

@inproceedings{cao-etal-2023-pay-attention,
    title = "Pay Attention to the Robustness of {C}hinese Minority Language Models! Syllable-level Textual Adversarial Attack on {T}ibetan Script",
    author = "Cao, Xi  and
      Dawa, Dolma  and
      Qun, Nuo  and
      Nyima, Trashi",
    booktitle = "Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.trustnlp-1.4",
    pages = "35--46"
}