@inproceedings{park-etal-2026-empirical,
    title = "An Empirical Study of On-Device Translation for Real-Time Live-Stream Chat on Mobile Devices",
    author = "Park, Jeiyoon  and
      Lee, Daehwan  and
      Yeo, Changmin  and
      Han, Yongshin  and
      Kim, Minseop",
    editor = "Zhang, Yue  and
      Xu, Ruifeng  and
      Purwarianti, Ayu  and
      Rong, Tong  and
      Suzuki, Jun",
    booktitle = "Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the {A}sia-Pacific Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 2: Short Papers)",
    month = nov,
    year = "2026",
    address = "Hengqin, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.aacl-short.1/",
    pages = "1--10",
    ISBN = "979-8-89176-440-8",
    abstract = "Despite its efficiency, there has been little research on the practical aspects required for real-world deployment of on-device AI models, such as the device{'}s CPU utilization and thermal conditions. In this paper, through extensive experiments, we investigate two key issues that must be addressed to deploy on-device models in real-world services: (i) the selection of on-device models and the resource consumption of each model, and (ii) the capability and potential of on-device models for domain adaptation. To this end, we focus on a task of translating live-stream chat messages and manually construct LiveChatBench, a benchmark consisting of 1,000 Korean{--}English parallel sentence pairs. Experiments on five mobile devices demonstrate that, although serving a large and heterogeneous user base requires careful consideration of highly constrained deployment settings and model selection, the proposed approach nevertheless achieves performance comparable to commercial models such as GPT-5.1 on the well-targeted task. The code, trained models, and LiveChatBench will be made publicly available at our GitHub."
}