ウクライナ語の現在の基礎的な大型言語モデルのトークン化効率
Daniil Maksymenko1, Oleksii Turuta2
1Department of Artificial Intelligence, Kharkiv National University of Radio Electronics, Kharkiv, Ukraine.
Frontiers in artificial intelligence
|August 29, 2025
まとめ
この研究は,ウクライナ語の大型言語モデル (LLM) のトークン化器を評価し,現在のモデルは低リソース言語には効率的ではないことを発見しました. 文字転写のアプローチは,ウクライナ語のモデリングの効率を向上させる可能性を示しています.
科学分野:
- 自然言語処理
- コンピュータ言語学
背景:
- 大型言語モデル (LLM) は,多言語環境でますます使用されています.
- トークン化効率は低リソース言語にとって大きな課題であり,速度,コスト,性能に影響します.
- ウクライナ語モデルは,既存のトークナイザー語彙に不足しているため,特定の障害に直面しています.
研究 の 目的:
- ウクライナ語専用のLLMを事前に訓練した様々なトークナライザーのパフォーマンスを比較する.
- 一般的およびドメイン特有のウクライナ語のテキストで最先端のモデルのトークン化率を測定する.
- データの損失なしにウクライナのトークン化効率を高めるための文字変換アプローチの有効性を調査する.
主な方法:
- ウクライナ語のテキストに適用された複数のトークン化器の比較分析
- 現在のSOTA LLMのトークン化生育率測定
- トークン化改善のための翻訳戦略の実験評価.
主要な成果:
- ウクライナ語の現在のLLMトークン化器の重要な非効率性を特定しました.
- 異なるモデルとウクライナ語のドメインで量化されたトークン化生育率.
- トークン化効率の潜在的改善を証明した.
結論:
- 既存のLLMトークン化器は,ウクライナ語のモデリングにデメリットがあります.
- この研究は,ウクライナ語のような低リソース言語の計算コストと処理速度に関する課題を強調しています.
- 文字変換は,トークン化による非効率性を軽減し,ウクライナ語のLLMアクセシビリティを改善するための実行可能な戦略を提供します.
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