Exploiting Vocabulary Frequency Imbalance in Language Model Pre-training
NeutralArtificial Intelligence
A recent study published on arXiv explores the impact of vocabulary size on language model pre-training. Researchers scaled the vocabulary from 24,000 to 196,000 tokens while keeping other factors constant. This investigation is crucial as it addresses the imbalance in token distribution, where a few words are used frequently while most are rarely seen. Understanding the benefits of larger vocabularies could enhance the effectiveness of language models, which are increasingly important in various applications, from chatbots to translation services.
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