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 proposeT-Freewhich directly embeds words through sparse activation patterns over character triplets, and does not require a reference corpus.T-Freeinherently 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-Freeshows significant improvements incross-lingual transfer learning.