Papers › XIFBench: Evaluating Large Language Models on Multilingual Instruction Following

XIFBench: Evaluating Large Language Models on Multilingual Instruction Following

10 Mar 2025arXiv:2503.07539archive 2025-07-28

Zhenyu Li, Kehai Chen, Yunfei Long, Xuefeng Bai, Yaoyin Zhang, Xuchen Wei, Juntao Li, Min Zhang

Large Language Models (LLMs) have demonstrated remarkable instruction-following capabilities across various applications. However, their performance in multilingual settings remains poorly understood, as existing evaluations lack fine-grained constraint analysis. We introduce XIFBench, a comprehensive constraint-based benchmark for assessing multilingual instruction-following abilities of LLMs, featuring a novel taxonomy of five constraint categories and 465 parallel instructions across six languages spanning different resource levels. To ensure consistent cross-lingual evaluation, we develop a requirement-based protocol that leverages English requirements as semantic anchors. These requirements are then used to validate the translations across languages. Extensive experiments with various LLMs reveal notable variations in instruction-following performance across resource levels, identifying key influencing factors such as constraint categories, instruction complexity, and cultural specificity.

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format_evaluation_requirements zhenyuli801/XIFBench/D2_validate_translations.py found in paper text by Syntology unverified MIT (permissive) · 3a43b74a35f656eb · report
format_sampled_constraints zhenyuli801/XIFBench/B2_combine_constraints.py found in paper text by Syntology unverified MIT (permissive) · 1cfb172de71361fd · report
generate_reasoning_and_responses zhenyuli801/XIFBench/G1_api_inference.py found in paper text by Syntology unverified MIT (permissive) · 635301a89612a3d4 · report
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sample_constraints zhenyuli801/XIFBench/B2_combine_constraints.py found in paper text by Syntology unverified MIT (permissive) · 33730eed64c4e455 · report

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