Papers with multilingual capabilities of
MEXA: Multilingual Evaluation of English-Centric LLMs via Cross-Lingual Alignment (2025.findings-acl)
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Amir Hossein Kargaran, Ali Modarressi, Nafiseh Nikeghbal, Jana Diesner, François Yvon, Hinrich Schuetze
| Challenge: | Existing benchmarks for multilinguality for English-centric large language models focus on classic tasks or cover a minimal number of languages. |
| Approach: | They propose a method to assess multilingual capabilities of pre-trained LLMs using parallel sentences. |
| Outcome: | The proposed method evaluates the multilingual capabilities of pre-trained English-centric models using parallel sentences. |
Just Go Parallel: Improving the Multilingual Capabilities of Large Language Models (2025.acl-long)
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| Challenge: | Large language models (LLMs) have impressive translation capabilities even without being explicitly trained on parallel data. |
| Approach: | They propose to add parallel data to enhance multilingual encoder-based and encoder decoder language models by focusing on translation and multilingual common-sense reasoning. |
| Outcome: | The proposed methods show that adding parallel data can significantly improve LLMs’ multilingual capabilities. |