Byte language models scale with emergent abstractions and information allocation; transformers can process raw byte sequences and outperform subword models as parameters grow
Read the original at arxiv.org→The paper challenges the assumption that language models need explicit tokenizers to be efficient demonstrating that standard flat Transformers can process raw byte sequences and actually outperform traditional...
Original headline: "Byte Language Models: Scaling, Emergent Abstractions, and Information Allocation"
Coverage timeline
- Oct 10, 22:25 UTC Lobsters (AI) lead source Byte Language Models: Scaling, Emergent Abstractions, and Information Allocation