arXiv:2606.25568v1 Announce Type: new 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 mathema
The increasing recognition of LLMs' mathematical reasoning capabilities, coupled with the linguistic bias of current models, highlights the urgent need for language-specific adaptations.
This development signals a critical step towards democratizing advanced AI capabilities beyond English, fostering localized innovation and reducing dependence on dominant linguistic AI frameworks.
The availability of specialized LLMs like Riazi-8B specifically for low-resource languages like Urdu enables advanced AI applications and mathematical reasoning for previously underserved populations.
- · Urdu-speaking users
- · AI developers in non-English speaking regions
- · Pakistan's technology sector
- · Educational technology providers
- · LLMs with exclusive English-centric training
- · Companies relying solely on English for advanced AI tools
- · Monolingual AI development paradigms
Urdu-speaking users gain access to more accurate and culturally relevant mathematical reasoning AI tools.
This spurs the development of similar language-specific LLMs for other low-resource languages, fostering a more linguistically diverse AI landscape.
Increased adoption of localized AI models could lead to new economic opportunities and educational advancements within these regions, potentially impacting global technology leadership.
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Read at arXiv cs.CL