Dynamic Multi-Byte Prediction With Hierarchical Language Models
The paper proposes predicting several bytes at once to speed byte-level hierarchical language models without adding parameters.
Its multi-byte prediction method uses variable-length windows aligned to the model’s latent segments. A new attention mask lets those bytes be predicted in parallel while preserving causality. The authors report a Pareto-best trade-off between output quality and inference throughput across generation, instruction following, QA, summarization, and translation. HF Daily Papers' note
Its multi-byte prediction method uses variable-length windows aligned to the model’s latent segments. A new attention mask lets those bytes be predicted in parallel while preserving causality. The authors report a Pareto-best trade-off between output quality and inference throughput across generation, instruction following, QA, summarization, and translation. HF Daily Papers' note
score 5