@inproceedings{zeng-etal-2025-marco,
title = "Marco-Bench-{MIF}: On Multilingual Instruction-Following Capability of Large Language",
author = "Zeng, Bo and
Lyu, Chenyang and
Liu, Sinuo and
Zeng, Mingyan and
Wu, Minghao and
Ni, Xuanfan and
Shi, Tianqi and
Zhao, Yu and
Liu, Yefeng and
Zhu, Chenyu and
Li, Ruizhe and
Geng, Jiahui and
Li, Qing and
Tong, Yu and
Wang, Longyue and
Luo, Weihua and
Zhang, Kaifu",
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.1172/",
doi = "10.18653/v1/2025.acl-long.1172",
pages = "24058--24072",
ISBN = "979-8-89176-251-0",
abstract = "Instruction-following capability has become a major ability to be evaluated for Large Language Models. However, existing datasets, such as IFEval, are either predominantly monolingual and centered on English or simply machine translated to other languages, limiting their applicability in multilingual contexts. In this paper, we present an carefully-curated extension of IFEval to a localized multilingual version named Marco-Bench-MIF, covering 30 languages with varying levels of localization. Our benchmark addresses linguistic constraints (e.g., modifying capitalization requirements for Chinese) and cultural references (e.g., substituting region-specific company names in prompts) via a hybrid pipeline combining translation with verification. Through comprehensive evaluation of 20+ LLMs on our Marco-Bench-MIF, we found that: (1) 25-35{\%} accuracy gap between high/low-resource languages, (2) model scales largely impact performance by 45-60{\%} yet persists script-specific challenges, and (3) machine-translated data underestimates accuracy by 7-22{\%} versus localized data. Our analysis identifies challenges in multilingual instruction following, including keyword consistency preservation and compositional constraint adherence across languages. Our Marco-Bench-MIF will be made publicly available to the community."
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<abstract>Instruction-following capability has become a major ability to be evaluated for Large Language Models. However, existing datasets, such as IFEval, are either predominantly monolingual and centered on English or simply machine translated to other languages, limiting their applicability in multilingual contexts. In this paper, we present an carefully-curated extension of IFEval to a localized multilingual version named Marco-Bench-MIF, covering 30 languages with varying levels of localization. Our benchmark addresses linguistic constraints (e.g., modifying capitalization requirements for Chinese) and cultural references (e.g., substituting region-specific company names in prompts) via a hybrid pipeline combining translation with verification. Through comprehensive evaluation of 20+ LLMs on our Marco-Bench-MIF, we found that: (1) 25-35% accuracy gap between high/low-resource languages, (2) model scales largely impact performance by 45-60% yet persists script-specific challenges, and (3) machine-translated data underestimates accuracy by 7-22% versus localized data. Our analysis identifies challenges in multilingual instruction following, including keyword consistency preservation and compositional constraint adherence across languages. Our Marco-Bench-MIF will be made publicly available to the community.</abstract>
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%0 Conference Proceedings
%T Marco-Bench-MIF: On Multilingual Instruction-Following Capability of Large Language
%A Zeng, Bo
%A Lyu, Chenyang
%A Liu, Sinuo
%A Zeng, Mingyan
%A Wu, Minghao
%A Ni, Xuanfan
%A Shi, Tianqi
%A Zhao, Yu
%A Liu, Yefeng
%A Zhu, Chenyu
%A Li, Ruizhe
%A Geng, Jiahui
%A Li, Qing
%A Tong, Yu
%A Wang, Longyue
%A Luo, Weihua
%A Zhang, Kaifu
%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 zeng-etal-2025-marco
%X Instruction-following capability has become a major ability to be evaluated for Large Language Models. However, existing datasets, such as IFEval, are either predominantly monolingual and centered on English or simply machine translated to other languages, limiting their applicability in multilingual contexts. In this paper, we present an carefully-curated extension of IFEval to a localized multilingual version named Marco-Bench-MIF, covering 30 languages with varying levels of localization. Our benchmark addresses linguistic constraints (e.g., modifying capitalization requirements for Chinese) and cultural references (e.g., substituting region-specific company names in prompts) via a hybrid pipeline combining translation with verification. Through comprehensive evaluation of 20+ LLMs on our Marco-Bench-MIF, we found that: (1) 25-35% accuracy gap between high/low-resource languages, (2) model scales largely impact performance by 45-60% yet persists script-specific challenges, and (3) machine-translated data underestimates accuracy by 7-22% versus localized data. Our analysis identifies challenges in multilingual instruction following, including keyword consistency preservation and compositional constraint adherence across languages. Our Marco-Bench-MIF will be made publicly available to the community.
%R 10.18653/v1/2025.acl-long.1172
%U https://aclanthology.org/2025.acl-long.1172/
%U https://doi.org/10.18653/v1/2025.acl-long.1172
%P 24058-24072
Markdown (Informal)
[Marco-Bench-MIF: On Multilingual Instruction-Following Capability of Large Language](https://aclanthology.org/2025.acl-long.1172/) (Zeng et al., ACL 2025)
ACL
- Bo Zeng, Chenyang Lyu, Sinuo Liu, Mingyan Zeng, Minghao Wu, Xuanfan Ni, Tianqi Shi, Yu Zhao, Yefeng Liu, Chenyu Zhu, Ruizhe Li, Jiahui Geng, Qing Li, Yu Tong, Longyue Wang, Weihua Luo, and Kaifu Zhang. 2025. Marco-Bench-MIF: On Multilingual Instruction-Following Capability of Large Language. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 24058–24072, Vienna, Austria. Association for Computational Linguistics.