The paper introduces Multilingual GSM‑Symbolic, a large multilingual math dataset with 30,000 question‑answer pairs across 15 languages, designed to evaluate cross‑lingual capability transfer using symbolic templates that generate millions of variations. It quantifies key determinants of transfer—model size, language resource level, reasoning ability, and typological distance—showing that larger models and better reasoning reduce performance gaps between low‑ and high‑resource languages, while typological distance has limited impact. The authors present a predictive framework that explains 92 % of language‑to‑language variation and can estimate unseen language performance within 6 pp, improving to 4.2 pp with only ten target‑language templates.
By Kenneth Enevoldsen, Riley Herchert, Sofie Mosegaard, Dan Saattrup Smart, Simon Enni, Isaac Chung, Sofie Bruun, Ayush Sunil Munot, Max M\"uller-Eberstein, Adnan El-Assadi, Elisa Bassignana, Gianluca Barmina, Hafsteinn Einarsson, Iben Nyholm Debess, Linda Freienthal, Lukas Galke Poech, Mike Zhang, Nicolas Legrand, Vladimir Salnikov, Yevhen Kostiuk, Zafar Hussain, Sagandeep Kaur, Agnes Toftg{\aa}rd, Marie Mattson, Kristoffer Nielbo
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
The evaluation of mathematical reasoning in large language models (LLMs) has predominantly focused on high-resource languages like English. This has created a significant barrier to the equitable development and deployment of AI in linguistically diverse regions such as Bangladesh, where over 230 million people speak Bengali.
arXiv:2605.30913v2 Announce Type: replace-cross
Abstract: Large language models (LLMs) are increasingly deployed in conversational settings where user phrasing ranges from polite to casual to toxic,...
By Soorya Ram Shimgekar, Agam Goyal, Amruta Parulekar, Joshua Chen, Yian Wang, Navin Kumar, Hari Sundaram, Eshwar Chandrasekharan, Koustuv Saha
arXiv:2607. 18266v1 Announce Type: new Abstract: Small language models are attractive for local deployment, but they often struggle with multi-step arithmetic reasoning.
By Jake O'Grady, Effirul Ramlan
arXiv:2509. 17314v4 Announce Type: replace-cross Abstract: Software increasingly relies on the emergent capabilities of Large Language Models (LLMs), from natural language understanding to program analysis and generation.
By Juyeon Yoon, Somin Kim, Robert Feldt, Shin Yoo