  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.
