目前乌克兰语言基础大型语言模型的代币化效率
Daniil Maksymenko1, Oleksii Turuta2
1Department of Artificial Intelligence, Kharkiv National University of Radio Electronics, Kharkiv, Ukraine.
Frontiers in artificial intelligence
|August 29, 2025
概括
这项研究评估了乌克兰语的大型语言模型 (LLM) 的代币化工具,发现当前的模型对低资源语言无效. 一种转写方法有望提高乌克兰语模拟效率.
科学领域:
- 自然语言处理
- 计算语言学
背景情况:
- 大型语言模型 (LLM) 在多语言环境中越来越多地使用.
- 代币化效率是低资源语言的主要挑战,影响速度,成本和性能.
- 由于在现有的代币化词汇中代表性不足,乌克兰语言模型面临着特殊的障碍.
研究的目的:
- 针对乌克兰语的预训练LLM进行各种代币化器的性能比较.
- 测量通用和特定领域的乌克兰文本中最先进的模型的代币化生长率.
- 调查转写方法的有效性,以提高乌克兰的代币化效率而不会丢失数据.
主要方法:
- 对乌克兰文本应用的多个tokenizers的比较分析.
- 目前SOTALLM的代币化生育率测量
- 对改进代币化的转写策略进行实验性评估.
主要成果:
- 确定了乌克兰语言的现有LLM代币化器的显著低效率.
- 在不同模型和乌克兰语言领域量化代币化生长率.
- 使用转写方法证明了代币化效率的潜在改进.
结论:
- 现有的LLM代币对乌克兰语言建模具有缺点.
- 这项研究强调了乌克兰语等资源较低语言的计算成本和处理速度的挑战.
- 转字提供了一种可行的策略来缓解代币化效率低下,并提高乌克兰的LLM可访问性.
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