  Tokenizers are crucial for encoding information in Large Language Models, but
their development has recently stagnated, and they contain inherent weaknesses.
Major limitations include computational overhead, ineffective vocabulary use,
and unnecessarily large embedding and head layers. Additionally, their
performance is biased towards a reference corpus, leading to reduced
effectiveness for underrepresented languages.
  To remedy these issues, we propose T-FREE, which directly embeds words
through sparse activation patterns over character triplets, and does not
require a reference corpus. T-FREE inherently exploits morphological
similarities and allows for strong compression of embedding layers. In our
exhaustive experimental evaluation, we achieve competitive downstream
performance with a parameter reduction of more than 85% on these layers.
Further, T-FREE shows significant improvements in cross-lingual transfer
learning.
