@inproceedings{liu-etal-2025-drop,
title = "A Drop-In Solution for On-the-Fly Adaptation of Speculative Decoding in Large Language Models",
author = "Liu, Jiesong and
Park, Brian and
Shen, Xipeng",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.482/",
doi = "10.18653/v1/2025.acl-long.482",
pages = "9778--9794",
ISBN = "979-8-89176-251-0",
abstract = "Large Language Models (LLMs) are cutting-edge generative AI models built on transformer architecture, which tend to be highly memory-intensive when performing real-time inference. Various strategies have been developed to enhance the end-to-end inference speed for LLMs, one of which is speculative decoding. This technique involves running a smaller LLM (draft model) for inference over a defined window size, denoted as $\gamma$, while simultaneously being validated by the larger LLM (target model). Choosing the optimal $\gamma$ value and the draft model is essential for unlocking the potential of speculative decoding. But it is difficult to do due to the complicated influence from various factors, including the nature of the task, the hardware in use, and the combination of the large and small models. This paper introduces *on-the-fly adaption of speculative decoding*, a solution that dynamically adapts the choices to maximize the efficiency of speculative decoding for LLM inferences. As a drop-in solution, it needs no offline benchmarking or training. Experiments show that the solution can lead to 3.55-16.48{\%} speed improvement over the standard speculative decoding, and 1.2-3.4$\times$ over the default LLMs."
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<abstract>Large Language Models (LLMs) are cutting-edge generative AI models built on transformer architecture, which tend to be highly memory-intensive when performing real-time inference. Various strategies have been developed to enhance the end-to-end inference speed for LLMs, one of which is speculative decoding. This technique involves running a smaller LLM (draft model) for inference over a defined window size, denoted as γ, while simultaneously being validated by the larger LLM (target model). Choosing the optimal γ value and the draft model is essential for unlocking the potential of speculative decoding. But it is difficult to do due to the complicated influence from various factors, including the nature of the task, the hardware in use, and the combination of the large and small models. This paper introduces *on-the-fly adaption of speculative decoding*, a solution that dynamically adapts the choices to maximize the efficiency of speculative decoding for LLM inferences. As a drop-in solution, it needs no offline benchmarking or training. Experiments show that the solution can lead to 3.55-16.48% speed improvement over the standard speculative decoding, and 1.2-3.4\times over the default LLMs.</abstract>
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%0 Conference Proceedings
%T A Drop-In Solution for On-the-Fly Adaptation of Speculative Decoding in Large Language Models
%A Liu, Jiesong
%A Park, Brian
%A Shen, Xipeng
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F liu-etal-2025-drop
%X Large Language Models (LLMs) are cutting-edge generative AI models built on transformer architecture, which tend to be highly memory-intensive when performing real-time inference. Various strategies have been developed to enhance the end-to-end inference speed for LLMs, one of which is speculative decoding. This technique involves running a smaller LLM (draft model) for inference over a defined window size, denoted as γ, while simultaneously being validated by the larger LLM (target model). Choosing the optimal γ value and the draft model is essential for unlocking the potential of speculative decoding. But it is difficult to do due to the complicated influence from various factors, including the nature of the task, the hardware in use, and the combination of the large and small models. This paper introduces *on-the-fly adaption of speculative decoding*, a solution that dynamically adapts the choices to maximize the efficiency of speculative decoding for LLM inferences. As a drop-in solution, it needs no offline benchmarking or training. Experiments show that the solution can lead to 3.55-16.48% speed improvement over the standard speculative decoding, and 1.2-3.4\times over the default LLMs.
%R 10.18653/v1/2025.acl-long.482
%U https://aclanthology.org/2025.acl-long.482/
%U https://doi.org/10.18653/v1/2025.acl-long.482
%P 9778-9794
Markdown (Informal)
[A Drop-In Solution for On-the-Fly Adaptation of Speculative Decoding in Large Language Models](https://aclanthology.org/2025.acl-long.482/) (Liu et al., ACL 2025)
ACL