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 standard as a tokenizer while also providing close to 2.5x faster training with more than 30% less training data.

本研究解决了大型语言模型训练过程中的资源浪费问题，提出了一种新的分词方法MultiTok，灵感来自于通用的LZW数据压缩。通过将重复短语压缩为多词令牌，MultiTok能够在保持相似准确性的同时，显著提高训练效率，实现了接近2.5倍的训练速度和超过30%的数据减少。

MultiTok：适应LZW压缩的可变长度分词方法用于高效的大型语言模型