Theo Datta, Kayla Huang, Sham Kakade, David Brandfonbrener
GQ-VAE is a new neural tokenizer that improves language model performance by encoding variable-length discrete tokens and can be used as a drop-in replacement for traditional tokenizers like BPE.
Traditional language models often use tokenization methods that rely on fixed rules, like byte-pair encoding (BPE), which can be limiting. The researchers developed a novel neural tokenizer called GQ-VAE, which learns to encode tokens of varying lengths, offering more flexibility and improved performance. This new method enhances both data compression and language model learning without requiring major changes to existing model architectures. GQ-VAE can be used as an easy replacement for current tokenizers, potentially leading to better language understanding in AI systems.
