@inproceedings{e1c38970cc5b48989fa0d6a5b6a39560,
title = "EFFIVLM-BENCH: A Comprehensive Benchmark for Evaluating Training-Free Acceleration in Large Vision-Language Models",
abstract = "Large Vision-Language Models (LVLMs) have achieved remarkable success, yet their significant computational demands hinder practical deployment. While efforts to improve LVLM efficiency are growing, existing methods lack comprehensive evaluation across diverse backbones, benchmarks, and metrics. In this work, we systematically evaluate mainstream acceleration techniques for LVLMs, categorized into token and parameter compression. We introduce EFFIVLM-BENCH, a unified framework for assessing not only absolute performance but also generalization and loyalty, while exploring Pareto-optimal trade-offs. Our extensive experiments and in-depth analyses offer insights into optimal strategies for accelerating LVLMs. We open-source code and recipes for EFFIVLM-BENCH to foster future research.",
author = "Zekun Wang and Minghua Ma and Zexin Wang and Rongchuan Mu and Liping Shan and Ming Liu and Bing Qin",
note = "Publisher Copyright: {\textcopyright} 2025 Association for Computational Linguistics.; 63rd Annual Meeting of the Association for Computational Linguistics, ACL 2025 ; Conference date: 27-07-2025 Through 01-08-2025",
year = "2025",
doi = "10.18653/v1/2025.acl-long.1242",
language = "英语",
series = "Proceedings of the Annual Meeting of the Association for Computational Linguistics",
publisher = "Association for Computational Linguistics (ACL)",
pages = "25546--25572",
editor = "Wanxiang Che and Joyce Nabende and Ekaterina Shutova and Pilehvar, \{Mohammad Taher\}",
booktitle = "Long Papers",
address = "澳大利亚",
}