MaXIFE: Multilingual and Cross-lingual Instruction Following Evaluation
Yile Liu, Ziwei Ma, Xiu Jiang, Jinglu Hu, ChangJing ChangJing, Liang Li
Abstract
With the rapid adoption of large language models (LLMs) in natural language processing, the ability to follow instructions has emerged as a key metric for evaluating their practical utility. However, existing evaluation methods often focus on single-language scenarios, overlooking the challenges and differences present in multilingual and cross-lingual contexts. To address this gap, we introduce MaXIFE: a comprehensive evaluation benchmark designed to assess instruction-following capabilities across 23 different languages with 1667 verifiable instruction tasks. MaXIFE integrates both Rule-Based Evaluation and Model-Based Evaluation, ensuring a balance of efficiency and accuracy. We applied MaXIFE to evaluate several leading commercial LLMs, establishing baseline results for future comparisons. By providing a standardized tool for multilingual instruction-following evaluation, MaXIFE aims to advance research and development in natural language processing.
BibTeX
@inproceedings{liu-etal-2025-maxife,
title = "{M}a{XIFE}: Multilingual and Cross-lingual Instruction Following Evaluation",
author = "Liu, Yile and
Ma, Ziwei and
Jiang, Xiu and
Hu, Jinglu and
ChangJing, ChangJing and
Li, Liang",
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.698/",
doi = "10.18653/v1/2025.acl-long.698",
pages = "14252--14332",
ISBN = "979-8-89176-251-0"
}