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Kosei Uemura

kosei.uemura@mila.quebec
Stagiaire de recherche - McGill
Superviseur⋅e principal⋅e
David Ifeoluwa Adelani
Sujets de recherche
Modèles génératifs
Traitement du langage naturel
Vision par ordinateur
GitHub

Publications

MGSM-Pro: A Simple Strategy for Robust Multilingual Mathematical Reasoning Evaluation
Tianyi Xu
Kosei Uemura
Alfred Malengo Kondoro
Tadesse Destaw Belay
Catherine Nana Nyaah Essuman
Ifeoma Okoh
Ganiyat Afolabi
Ayodele Awokoya
David Ifeoluwa Adelani
Large language models have made substantial progress in mathematical reasoning. However, benchmark development for multilingual evaluation h… (voir plus)as lagged behind English in both difficulty and recency. Recently, GSM-Symbolic showed a strong evidence of high variance when models are evaluated on different instantiations of the same question; however, the evaluation was conducted only in English. In this paper, we introduce MGSM-Pro, an extension of MGSM dataset with GSM-Symbolic approach. Our dataset provides five instantiations per MGSM question by varying names, digits and irrelevant context. Evaluations across nine languages reveal that many low-resource languages suffer large performance drops when tested on digit instantiations different from those in the original test set. We further find that models robustness in HRL setting do not necessarily translate to LRL. Moreover, proprietary models, such as Gemini 2.5 Flash and GPT-4.1 are less robust to digit, whereas Gemini 3.0 Pro is more robust. Among open models, GPT-OSS 120B and DeepSeek v3 show stronger robustness. Based on these findings, we recommend evaluating each problem using at least five digit-varying instantiations to obtain a more robust and realistic assessment of math reasoning.
2026-01-28
arXiv (prépublication)
doi.org
arxiv.org
MERLIN: Multi-Stage Curriculum Alignment for Multilingual Encoder-LLM Integration in Cross-Lingual Reasoning
Kosei Uemura
David Guzmán
Quang Phuoc Nguyen
Jesujoba Oluwadara Alabi
En-Shiun Annie Lee
David Ifeoluwa Adelani
Large language models excel in English but still struggle with complex reasoning in many low-resource languages (LRLs). Existing encoder-plu… (voir plus)s-decoder methods such as LangBridge and MindMerger raise accuracy on mid and high-resource languages, yet they leave a large gap on LRLs. We present MERLIN, a two-stage model-stacking framework that applies a curriculum learning strategy -- from general bilingual bitext to task-specific data -- and adapts only a small set of DoRA weights. On the AfriMGSM benchmark MERLIN improves exact-match accuracy by +12.9 pp over MindMerger and outperforms GPT-4o-mini. It also yields consistent gains on MGSM and MSVAMP (+0.9 and +2.8 pp), demonstrating effectiveness across both low and high-resource settings.
2025-12-31
Conference of the European Chapter of the Association for Computational Linguistics (publié)
doi.org
arxiv.org
AfriMTEB and AfriE5: Benchmarking and Adapting Text Embedding Models for African Languages
Kosei Uemura
Miaoran Zhang
David Ifeoluwa Adelani
2025-10-26
ArXiv (prépublication)
doi.org
arxiv.org