Language models typically tokenize raw text into sequences of subword
identifiers from a predefined vocabulary, a process inherently sensitive to
typographical errors, length variations, and largely oblivious to the internal
structure of tokens-issues we term the curse of tokenization. In this study, we
delve into these drawbacks and demonstrate that large language models (LLMs)
remain susceptible to these problems. This study systematically investigates
these challenges and their impact on LLMs through three critical research
questions: (1) complex problem solving, (2) token structure probing, and (3)
resilience to typographical variation. Our findings reveal that scaling model
parameters can mitigate the issue of tokenization; however, LLMs still suffer
from biases induced by typos and other text format variations. Our experiments
show that subword regularization such as BPE-dropout can mitigate this issue.
We will release our code and data to facilitate further research.

大型语言模型存在分词问题，导致对错别字、长度差异和标记内部结构的忽视。本研究通过探究复杂问题解决、标记结构探测和对错别字的抵抗力来系统地调查这些挑战及其对大型语言模型的影响，并展示模型参数缩放与子词规范化对解决这些问题的作用。

分词不足：分词的诅咒

Tokenization Falling Short: The Curse of Tokenization

Subword segmentation is widely used to address the open vocabulary problem in
machine translation. The dominant approach to subword segmentation is Byte Pair
Encoding (BPE), which keeps the most frequent words intact while splitting the
rare ones into multiple tokens. While multiple segmentations are possible even
with the same vocabulary, BPE splits words into unique sequences; this may
prevent a model from better learning the compositionality of words and being
robust to segmentation errors. So far, the only way to overcome this BPE
imperfection, its deterministic nature, was to create another subword
segmentation algorithm (Kudo, 2018). In contrast, we show that BPE itself
incorporates the ability to produce multiple segmentations of the same word. We
introduce BPE-dropout - simple and effective subword regularization method
based on and compatible with conventional BPE. It stochastically corrupts the
segmentation procedure of BPE, which leads to producing multiple segmentations
within the same fixed BPE framework. Using BPE-dropout during training and the
standard BPE during inference improves translation quality up to 3 BLEU
compared to BPE and up to 0.9 BLEU compared to the previous subword
regularization.

本研究提出了一种名为 BPE-dropout 的简单有效的子词规则化方法，通过随机扰动 BPE 的分词过程，在相同的 BPE 框架下生成多个分词方法，使用 BPE-dropout 能够显著提高机器翻译质量，比使用 BPE 和其他子词规则化方法均能够带来更好的效果。