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arXiv · 2506.01776

MaXIFE: Multilingual and Cross-lingual Instruction Following Evaluation

Yile Liu·Ziwei Ma·Xiu Jiang·Jinglu Hu·Jing Chang·Liang Li
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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.

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Yile Liu, Ziwei Ma, Xiu Jiang, Jinglu Hu, Jing Chang, Liang Li. 2025-06-02. MaXIFE: Multilingual and Cross-lingual Instruction Following Evaluation. https://arxiv.org/abs/2506.01776

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