AI 中文总结
本文分析5款可在商用硬件运行的LLMs对时间、数字、日期的定位能力，发现将定位原则嵌入提示上下文可显著提升其定位准确率。
AI 中文摘要
本文扩展了Tang等人（2025）关于数值翻译的研究，分析了5款可在商用硬件上加载并运行的大语言模型（LLMs）对时间、数字和日期的定位能力（而非翻译能力）。计算了各模型的基线质量后，测试了3种提升准确率的策略。与Tang等人的研究不同，本研究发现，在测试的LLMs中，将定位原则嵌入提示上下文，相比直接翻译或其他替代策略，能带来具有统计显著性的准确率提升。
英文摘要
The work of Tang et. al. (2025) on numerical translation is extended by analysing the capability of five large language models (LLMs) for the localisation of times, numbers, and dates instead of translation. Models were selected that could be loaded onto and run on commodity hardware and a baseline quality for each mode is computed, then three different strategies to improve on that accuracy were tested. In contrast to Tang et. al., it was discovered that on the tested LLMs, embedding the localisation principles into the prompt context provided a statistically significant improvement in accuracy compared to direct translation or the alternative strategies.
Comments13 pages, 7 tables, 2 figures
