MultiTok: LZW圧縮に基づく効率的なLLMのための可変長トークン化
原題: MultiTok: Variable-Length Tokenization for Efficient LLMs Adapted from LZW Compression
分析結果
- カテゴリ
- AI
- 重要度
- 85
- トレンドスコア
- 34
- 要約
- 大規模言語モデルは、より複雑な自然言語処理のための技術を導入することでAIの展望を大きく変えました。しかし、現在の方法には課題があります。
- キーワード
- 長期重要性
- 数年で重要
- ビジネス可能性
- 高い - 新しいトークン化手法は、AIモデルの開発コストを削減し、商業利用が期待できる。
- 日本波及可能性
- 高 - 日本のAI産業においても、効率的なトレーニング手法は競争力を高めるため重要である。
arXiv:2410.21548v3 Announce Type: replace-cross Abstract: Large language models have drastically changed the prospects of AI by introducing technologies for more complex natural language processing. However, current methodologies to train such LLMs require extensive resources including but not limited to large amounts of data, expensive machinery, and lengthy training. To solve this problem, this paper proposes a new tokenization method inspired by universal Lempel-Ziv-Welch data compression that compresses repetitive phrases into multi-word tokens. With MultiTok as a new tokenizing tool, we show that language models are able to be trained notably more efficiently while offering a similar accuracy on more succinct and compressed training data. In fact, our results demonstrate that MultiTok achieves a comparable performance to the BERT and GPT standards as both a stand-alone tokenizer and an add-on to existing tokenizers while also providing close to 2.5x faster training with more than 30% less training data. arXiv:2410.21548v3 Announce Type: replace-cross Abstract: Large language models have drastically changed the prospects of AI by introducing technologies for more complex natural language processing. However, current methodologies to train such LLMs require extensive resources including but not limited to large amounts of data, expensive machinery, and lengthy training. To solve this problem, this paper proposes a new tokenization method inspired by universal Lempel-Ziv-Welch data compression that compresses repetitive phrases into multi-word tokens. With MultiTok as a new tokenizing tool, we show that language models are able to be trained notably more efficiently while offering a similar accuracy on more succinct and compressed training data. In fact, our results demonstrate that MultiTok achieves a comparable performance to the BERT and GPT standards as both a stand-alone tokenizer and an add-on to existing tokenizers while also providing close to 2.5x faster training with more than 30% less training data.