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 multilingual LLMs in both general (MMLU benchmark) and domain-specific (Harry Potter quiz and TOFU benchmark) contexts, and proposes to mitigate the barrier via mixed-language fine-tuning.

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 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 and TOFU benchmark) contexts. Since simple inference-time mitigation methods 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.
Our code is available at https://github.com/google-research/crosslingual-knowledge-barriers.

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Submission Number: 1429