Towards Fast Multilingual LLM Inference: Speculative Decoding and Specialized Drafters
Euiin Yi, Taehyeon Kim, Hongseok Jeung, Du-Seong Chang, Se-Young Yun
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.
BibTeX
@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://aclanthology.org/2024.emnlp-main.602/",
doi = "10.18653/v1/2024.emnlp-main.602",
pages = "10789--10802"
}