Byte Language Models: Scaling, Emergent Abstractions, and Information Allocation
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 subword models as parameter sizes scale.
Key points
- A typical subword contains around four bytes.
- By implementing token-superposition training and hash embeddings, the byte models consistently hit lower optimal loss than subword models at matched parameter counts.
- It's also interesting to see how the byte model handles the lack of explicit text abstractions.
- We usually rely on tokenizers to group characters into meaningful chunks, but it turns out that byte Transformers implicitly develop their own local abstractions without needing specialized hierarchical architectures.
Sources (1)
- [1]Byte Language Models: Scaling, Emergent Abstractions, and Information AllocationLobsters: ai · Oct 10, 10:25 PM
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 subword models as parameter sizes scale.
A typical subword contains around four bytes.
Extractive summary: sentences quoted from the sources.
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