# MaXIFE: Multilingual and Cross-lingual Instruction Following Evaluation

arXiv: https://arxiv.org/abs/2506.01776 · HTML: https://mlsanity.com/paper/2506.01776/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2025.

## Built on by (most cited first, 7 of 7)

- [Hy-MT2: A Family of Fast, Efficient and Powerful Multilingual Translation Models in the Wild](https://mlsanity.com/paper/2605.22064/) (2026)
- [Revisiting the Reliability of Language Models in Instruction-Following](https://mlsanity.com/paper/2512.14754/) (2025)
- [Deconstructing Instruction-Following: A New Benchmark for Granular Evaluation of Large Language Model Instruction Compliance Abilities](https://mlsanity.com/paper/2601.18554/) (2026)
- [ChiKhaPo: A Large-Scale Multilingual Benchmark for Evaluating Lexical Comprehension and Generation in Large Language Models](https://mlsanity.com/paper/2510.16928/) (2025)
- [IndicIFEval: A Benchmark for Verifiable Instruction-Following Evaluation in 14 Indic Languages](https://mlsanity.com/paper/2602.22125/) (2026)
- [EstLLM: Enhancing Estonian Capabilities in Multilingual LLMs via Continued Pretraining and Post-Training](https://mlsanity.com/paper/2603.02041/) (2026)
- [Disentangling Language Roles in Multilingual LLM Task Execution](https://mlsanity.com/paper/2605.27649/) (2026)

## Abstract (arXiv)

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.