The inaccurate translation of numbers can lead to significant security issues, ranging from financial setbacks to medical inaccuracies. While large language models (LLMs) have made significant advancements in machine translation, their capacity for translating numbers has not been thoroughly explored. This study focuses on evaluating the reliability of LLM-based machine translation systems when handling numerical data. In order to systematically test the numerical translation capabilities of currently open source LLMs, we have constructed a numerical translation dataset between Chinese and English based on real business data, encompassing ten types of numerical translation. Experiments on the dataset indicate that errors in numerical translation are a common issue, with most open-source LLMs faltering when faced with our test scenarios. Especially when it comes to numerical types involving large units like ``million", ``billion", and "yi", even the latest llama3.1 8b model can have error rates as high as 20%. Finally, we introduce three potential strategies to mitigate the numerical mistranslations for large units.

本研究针对大型语言模型在数字翻译中存在的可靠性问题，尤其是在处理涉及大单位（如“百万”、“十亿”和“亿”）的数字时，其错误率高达20%。通过构建基于真实商业数据的中英文数字翻译数据集，系统评估当前开源大型语言模型的翻译能力，同时提出了三种策略以减轻数字翻译错误的影响。

大型语言模型的数字翻译研究