Riazi-8B: An Urdu Large Language Model for Mathematical Reasoning
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Computer Science > Computation and Language
Title:Riazi-8B: An Urdu Large Language Model for Mathematical Reasoning
Abstract:Recent LLMs demonstrate strong mathematical reasoning capabilities, but existing gains rely heavily on English-centric training resources and benchmarks. As a result, reasoning performance degrades substantially in low-resource languages such as Urdu, where reasoning-oriented datasets and adapted models remain scarce. Urdu lacks both reasoning-oriented resources and models adapted for multi-step mathematical problem solving, limiting the applicability of recent progress to Urdu-speaking users. We address this gap through Riazi-8B, an Urdu mathematical reasoning model developed through a two-step adaptation process comprising continued pre-training on Urdu Wikipedia and supervised fine-tuning on Urdu Chain-of-Thought data derived from GSM8K. We evaluate Riazi-8B on MGSM-Urdu against existing Urdu instruction-tuned models. Our results show consistent improvements in answer correctness, reasoning quality, response completeness, and Urdu generation. Our findings demonstrate that combining Urdu language adaptation with reasoning-focused fine-tuning is an effective strategy for extending mathematical reasoning capabilities to low-resource languages.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.25568 [cs.CL] |
| (or arXiv:2606.25568v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.25568
arXiv-issued DOI via DataCite (pending registration)
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