MaXIFE: Multilingual and Cross-lingual Instruction Following Evaluation

TL;DR

MaXIFE is a new benchmark for evaluating multilingual and cross-lingual instruction-following abilities of large language models across 23 languages with 1667 tasks, combining rule-based and model-based assessments.

Contribution

It introduces MaXIFE, the first comprehensive multilingual and cross-lingual instruction-following evaluation benchmark with a balanced evaluation approach.

Findings

Baseline results for several commercial LLMs established.

Demonstrates the effectiveness of combined evaluation methods.

Highlights challenges in multilingual instruction following.

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.