# Bridging the Culture Gap: A Framework for LLM-Driven Socio-Cultural Localization of Math Word Problems in Low-Resource Languages

arXiv: https://arxiv.org/abs/2508.14913 · HTML: https://mlsanity.com/paper/2508.14913/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 29 references that are in the map.
- First posted on arXiv 13 Aug 2025.

## Builds on (most cited first, 16 of 16)

- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Language Models are Multilingual Chain-of-Thought Reasoners](https://mlsanity.com/paper/2210.03057/) (2022)
- [CMMLU: Measuring massive multitask language understanding in Chinese](https://mlsanity.com/paper/2306.09212/) (2023)
- [FineWeb2: One Pipeline to Scale Them All -- Adapting Pre-Training Data Processing to Every Language](https://mlsanity.com/paper/2506.20920/) (2025)
- [KMMLU: Measuring Massive Multitask Language Understanding in Korean](https://mlsanity.com/paper/2402.11548/) (2024)
- [ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic](https://mlsanity.com/paper/2402.12840/) (2024)
- [A Survey of Mathematical Reasoning in the Era of Multimodal Large Language Model: Benchmark, Method & Challenges](https://mlsanity.com/paper/2412.11936/) (2024)
- [Revisiting Zero-Shot Abstractive Summarization in the Era of Large Language Models from the Perspective of Position Bias](https://mlsanity.com/paper/2401.01989/) (2024)
- [What Makes Math Word Problems Challenging for LLMs?](https://mlsanity.com/paper/2403.11369/) (2024)
- [Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Ahead](https://mlsanity.com/paper/2505.21315/) (2025)
- [ArithmAttack: Evaluating Robustness of LLMs to Noisy Context in Math Problem Solving](https://mlsanity.com/paper/2501.08203/) (2025)
- [Assessing LLMs for Zero-shot Abstractive Summarization Through the Lens of Relevance Paraphrasing](https://mlsanity.com/paper/2406.03993/) (2024)
- [INJONGO: A Multicultural Intent Detection and Slot-filling Dataset for 16 African Languages](https://mlsanity.com/paper/2502.09814/) (2025)
- [Lost in Cultural Translation: Do LLMs Struggle with Math Across Cultural Contexts?](https://mlsanity.com/paper/2503.18018/) (2025)
- [Watching the AI Watchdogs: A Fairness and Robustness Analysis of AI Safety Moderation Classifiers](https://mlsanity.com/paper/2501.13302/) (2025)
- [Mathematics Isn't Culture-Free: Probing Cultural Gaps via Entity and Scenario Perturbations](https://mlsanity.com/paper/2507.00883/) (2025)

## Built on by (most cited first, 1 of 1)

- [AfrIFact: Cultural Information Retrieval, Evidence Extraction and Fact Checking for African Languages](https://mlsanity.com/paper/2604.00706/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have demonstrated significant capabilities in solving mathematical problems expressed in natural language. However, multilingual and culturally-grounded mathematical reasoning in low-resource languages lags behind English due to the scarcity of socio-cultural task datasets that reflect accurate native entities such as person names, organization names, and currencies. Existing multilingual benchmarks are predominantly produced via translation and typically retain English-centric entities, owing to the high cost associated with human annotater-based localization. Moreover, automated localization tools are limited, and hence, truly localized datasets remain scarce. To bridge this gap, we introduce a framework for LLM-driven cultural localization of math word problems that automatically constructs datasets with native names, organizations, and currencies from existing sources. We find that translated benchmarks can obscure true multilingual math ability under appropriate socio-cultural contexts. Through extensive experiments, we also show that our framework can help mitigate English-centric entity bias and improves robustness when native entities are introduced across various languages.