
Dynamic Token Merging for Efficient Byte-level Language Models with Julie Kallini - #724
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
The Intricacies of Tokenization in Language Models
This chapter examines the crucial role of tokenization in language models, discussing the challenges it presents, particularly for underrepresented languages. It highlights comparisons between tokenization methods and their impacts on model efficiency and performance. The experts also delve into innovative architectures that enhance token management and processing, emphasizing the importance of adapting solutions to diverse linguistic structures.
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