Crosslingual Capabilities and Knowledge Barriers in Multilingual Large Language Models

Lynn Chua, Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chulin Xie, Chiyuan Zhang

Keywords: Large Language Models, Multilingual, Crosslingual Knowledge Barrier

TL;DR: This study reveals the crosslingual knowledge barrier for six state-of-the-art LLMs and proposes to mitigate the barrier via fine-tuning on mixed-language data.

Abstract: Large language models (LLMs) are typically multilingual due to pretraining on diverse multilingual corpora. But can these models relate corresponding concepts across languages, i.e., be crosslingual? This study evaluates six state-of-the-art LLMs on inherently crosslingual tasks. We observe that while these models show promising surface-level crosslingual abilities on machine translation and embedding space analyses, they struggle with deeper crosslingual knowledge transfer, revealing a crosslingual knowledge barrier in both general (MMLU benchmark) and domain-specific (Harry Potter quiz) contexts. Since simple inference-time mitigation methods seem to offer only limited improvement, we propose fine-tuning of LLMs on mixed-language data, which effectively reduces these gaps, even when using out-of-domain datasets like WikiText. Our findings suggest the need for explicit optimization to unlock the full crosslingual potential of LLMs.

Primary Area: foundation or frontier models, including LLMs

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Submission Number: 7405