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Found 2 papers, 1 papers with code
Bridging the Gap for Tokenizer-Free Language Models
Purely character-based language models (LMs) have been lagging in quality on large scale datasets, and current state-of-the-art LMs rely on word tokenization.
Character-Level Language Modeling with Deeper Self-Attention
LSTMs and other RNN variants have shown strong performance on character-level language modeling.
Ranked #8 on
Language Modelling
on Hutter Prize
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