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    <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.</abstract>
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%0 Conference Proceedings
%T MaXIFE: Multilingual and Cross-lingual Instruction Following Evaluation
%A Liu, Yile
%A Ma, Ziwei
%A Jiang, Xiu
%A Hu, Jinglu
%A ChangJing, ChangJing
%A Li, Liang
%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-maxife
%X 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.
%R 10.18653/v1/2025.acl-long.698
%U https://aclanthology.org/2025.acl-long.698/
%U https://doi.org/10.18653/v1/2025.acl-long.698
%P 14252-14332

Markdown (Informal)

[MaXIFE: Multilingual and Cross-lingual Instruction Following Evaluation](https://aclanthology.org/2025.acl-long.698/) (Liu et al., ACL 2025)

• MaXIFE: Multilingual and Cross-lingual Instruction Following Evaluation (Liu et al., ACL 2025)

ACL

• Yile Liu, Ziwei Ma, Xiu Jiang, Jinglu Hu, ChangJing ChangJing, and Liang Li. 2025. MaXIFE: Multilingual and Cross-lingual Instruction Following Evaluation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 14252–14332, Vienna, Austria. Association for Computational Linguistics.