arXiv:2607.05992v1 Announce Type: cross Abstract: Mathematical reasoning has become a central task for evaluating and tuning reasoning Large Language Models (LLMs), yet existing benchmarks remain heavily biased toward high-resource languages, with English and Chinese dominating both pre-training corpora and evaluation suites. The recently released PolyMath (Wang et al., 2025) dataset represents a significant step forward, yet its coverage is still limited to 18 only high-resource languages. To address this gap, we introduce PluraMath, an extension of PolyMath to 18 additional {underrepresented
The increased focus on LLM evaluation in diverse linguistic contexts highlights limitations in existing benchmarks, driving the creation of more inclusive datasets like PluraMath.
Sophisticated readers should care because expanding mathematical reasoning evaluation to underrepresented languages is critical for developing truly global and equitable AI capabilities.
The availability of PluraMath means that LLM development and evaluation can now progress beyond high-resource language biases, potentially leading to more robust and culturally aware AI models.
- · AI developers in non-English/Chinese regions
- · LLM developers focusing on multilingual capabilities
- · Linguistically diverse communities
- · Monolingual AI evaluation methodologies
- · AI models without diverse linguistic exposure
PluraMath directly addresses the linguistic bias in LLM mathematical reasoning benchmarks by extending coverage to underrepresented languages.
This extended evaluation capability could lead to the development of more diverse and robust LLMs capable of performing complex reasoning across many languages, reducing the dominance of high-resource language models.
Improved multilingual LLMs could accelerate AI adoption and innovation in previously underserved regions, potentially fostering sovereign AI capabilities and reducing dependency on models trained exclusively on English or Chinese data.
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Read at arXiv cs.AI