Understand, Solve and Translate: Bridging the Multilingual Mathematical Reasoning Gap

• URL: http://arxiv.org/abs/2501.02448v1
• Date: Sun, 05 Jan 2025 05:57:22 GMT
• Title: Understand, Solve and Translate: Bridging the Multilingual Mathematical Reasoning Gap
• Authors: Hyunwoo Ko, Guijin Son, Dasol Choi,
• Abstract summary: Large language models (LLMs) demonstrate exceptional performance on complex reasoning tasks.<n>Despite strong reasoning capabilities in high-resource languages, a significant performance gap persists in other languages.<n>We propose UST (Understand, Solve, and Translate), a method that strategically uses English as an anchor for reasoning and solution generation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large language models (LLMs) demonstrate exceptional performance on complex reasoning tasks. However, despite their strong reasoning capabilities in high-resource languages (e.g., English and Chinese), a significant performance gap persists in other languages. To investigate this gap in Korean, we introduce HRM8K, a benchmark comprising 8,011 English-Korean parallel bilingual math problems. Through systematic analysis of model behaviors, we identify a key finding: these performance disparities stem primarily from difficulties in comprehending non-English inputs, rather than limitations in reasoning capabilities. Based on these findings, we propose UST (Understand, Solve, and Translate), a method that strategically uses English as an anchor for reasoning and solution generation. By fine-tuning the model on 130k synthetically generated data points, UST achieves a 10.91% improvement on the HRM8K benchmark and reduces the multilingual performance gap from 11.6% to 0.7%. Additionally, we show that improvements from UST generalize effectively to different Korean domains, demonstrating that capabilities acquired from machine-verifiable content can be generalized to other areas. We publicly release the benchmark, training dataset, and models.

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