arXiv AI By Noor Abo Mokh, Teresa Lynn, Nizar Habash, Ahmed Elshabrawy, Sara Alhajeri, Alaa Elsetohy, Preslav Nakov, Alham Fikri Aji

Benchmarking Arabic Dialects for Math Problems

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arXiv AI
Sep 30

MGSM-Pro: A Simple Strategy for Robust Multilingual Mathematical Reasoning Evaluation

arXiv:2601.21225v4 Announce Type: replace-cross Abstract: Large language models have made substantial progress in mathematical reasoning. However, benchmark development for multilingual evaluation ha...

By Tianyi Xu, Kosei Uemura, Alfred Malengo Kondoro, Tadesse Destaw Belay, Catherine Nana Nyaah Essuman, Ifeoma Okoh, Ganiyat Afolabi, Ayodele Awokoya, David Ifeoluwa Adelani
arXiv AI
Jul 8

PluraMath: Extending Mathematical Reasoning Evaluation Beyond High-Resource Languages

arXiv:2607. 05992v1 Announce Type: cross Abstract: Mathematical reasoning has become a central task for evaluating and tuning reasoning Large Language Models (LLMs), yet existing benchmarks remain heavily biased toward high-resource languages, with English and Chinese dominating both pre-training corpora and evaluation suites.

By Daryna Dementieva, Nikolay Babakov, Kathy H\"ammerl, Ilseyar Alimova, Jind\v{r}ich Libovick\'y, Shu Okabe, Miras Baisbay, Lukas Edman, Abrorkhon Inomkhujaev, Antonia Karamolegkou, Mateusz Lango, Volkan \"Ozer, Nikola Selic, Subhankar Swain, Tsedeniya Kinfe Temesgen, Galit Bary Weisberg, Alexander Fraser
arXiv Computation and Language
Aug 27

Rethinking the Multilingual Reasoning Gap with Layer Swap

The study investigates the performance gap between native-language reasoning and English-pivoted reasoning in large language models. By creating extensive multilingual reasoning datasets and fine‑tuning specialists on Qwen/Qwen3-8B-Base, the authors find that the native reasoning gap is much smaller (1.9–3.5%) than previously reported. They analyze weight‑space changes, discover a language‑agnostic reasoning core in the middle layers, and propose a Layer Swap technique that transfers these mid‑layer updates from an English specialist to native specialists, effectively closing most of the gap while maintaining native chain‑of‑thought output.

By Maxence Lasbordes, Am\'elie Chatelain, Djam\'e Seddah
arXiv Computation and Language
6d ago

How Well Does Mathematical Reasoning Transfer Across Languages in English-Centric LLMs?

The paper investigates how mathematical reasoning improvements from Reinforcement Post-Training (RPT) in English-centric large language models (LLMs) transfer to other languages. Experiments show that transfer varies greatly depending on the initial model, target language, and training approach, and that stronger English initialization does not guarantee better transfer efficiency. Adding a single parallel language to the training data yields the largest boost, with additional languages providing smaller but consistent gains.

By Wen Yang, Junhong Wu, Chong Li, Chengqing Zong, Jiajun Zhang