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Riazi-8B: Urdu LLM enhances mathematical reasoning for low-resource languages

By PulseAugur Editorial · [2 sources] ·

Researchers have developed Riazi-8B, a new large language model specifically designed for mathematical reasoning in the Urdu language. This model addresses the limitations of existing English-centric LLMs, which perform poorly on low-resource languages like Urdu. Riazi-8B was created through a two-step process involving pre-training on Urdu Wikipedia and fine-tuning with Urdu Chain-of-Thought data derived from GSM8K. Evaluations on the MGSM-Urdu benchmark show that Riazi-8B significantly improves answer correctness, reasoning quality, and Urdu generation compared to other Urdu instruction-tuned models. AI

IMPACT Extends mathematical reasoning capabilities of LLMs to low-resource languages, potentially benefiting Urdu-speaking users and informing future multilingual AI development.

RANK_REASON The cluster describes a new research paper detailing the creation and evaluation of a specialized LLM for a low-resource language.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Riazi-8B: Urdu LLM enhances mathematical reasoning for low-resource languages

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The cluster describes a new research paper detailing the creation and evaluation of a specialized LLM for a low-resource language.
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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Azher Ali, Ibtsam Haider, Raja Khurram Shahzad, Seemab Latif, Mehwish Fatima ·

    Riazi-8B: An Urdu Large Language Model for Mathematical Reasoning

    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 lang…

  2. arXiv cs.CL TIER_1 English(EN) · Mehwish Fatima ·

    Riazi-8B: An Urdu Large Language Model for Mathematical Reasoning

    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 dat…

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