| Challenge: | a fair comparison of language models is tricky because of the size of the corpora and the variability of orthographic systems. |
| Approach: | They propose a framework for fair cross-linguistic comparison of language models . they show that in some languages, textual expression is harder to predict with n-gram models compared to LSTM models based on translated text . |
| Outcome: | The proposed framework is based on translated text and language models on 21 languages. |
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| Challenge: | a recent study suggests that language models perform poorly across languages. |
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| Challenge: | a key challenge in cross-lingual NLP is developing general language-independent architectures that are equally applicable to any language. |
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| Challenge: | Existing studies on inflectional morphology disagree on whether or not it makes languages harder to model. |
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| Challenge: | Existing evidence on the intrinsic difficulty of multilingual modeling is limited to small monolingual models or bilingual models trained from scratch. |
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Md Tahmid Rahman Laskar, Sawsan Alqahtani, M Saiful Bari, Mizanur Rahman, Mohammad Abdullah Matin Khan, Haidar Khan, Israt Jahan, Amran Bhuiyan, Chee Wei Tan, Md Rizwan Parvez, Enamul Hoque, Shafiq Joty, Jimmy Huang
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| Challenge: | Large language models (LLMs) exhibit uneven performance across languages. |
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The Linguistic Connectivities Within Large Language Models (2025.findings-acl)
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Dan Wang, Boxi Cao, Ning Bian, Xuanang Chen, Yaojie Lu, Hongyu Lin, Jia Zheng, Le Sun, Shanshan Jiang, Bin Dong, Xianpei Han
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