@inproceedings{yi-etal-2024-towards,
    title = "Towards Fast Multilingual {LLM} Inference: Speculative Decoding and Specialized Drafters",
    author = "Yi, Euiin  and
      Kim, Taehyeon  and
      Jeung, Hongseok  and
      Chang, Du-Seong  and
      Yun, Se-Young",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2024.emnlp-main.602/",
    doi = "10.18653/v1/2024.emnlp-main.602",
    pages = "10789--10802",
    abstract = "Large language models (LLMs) have revolutionized natural language processing and broadened their applicability across diverse commercial applications. However, the deployment of these models is constrained by high inference time in multilingual settings. To mitigate this challenge, this paper explores a training recipe of an assistant model in speculative decoding, which are leveraged to draft and-then its future tokens are verified by the target LLM. We show that language-specific draft models, optimized through a targeted pretrain-and-finetune strategy, substantially brings a speedup of inference time compared to the previous methods. We validate these models across various languages in inference time, out-of-domain speedup, and GPT-4o evaluation."
}