New multi-byte prediction method speeds up hierarchical language models

Researchers have developed a new method called multi-byte prediction (MBP) to accelerate the inference speed of hierarchical language models. This technique generates multiple bytes in parallel, offering a significant speed-up with minimal impact on performance and without requiring additional parameters. MBP enhances the multi-token prediction paradigm by incorporating a variable-length prediction window and a novel attention-masking scheme to maintain causality during parallel byte generation. Evaluations across various generative tasks, including instruction following, question answering, summarization, and machine translation, demonstrate that MBP achieves an optimal balance between performance and inference throughput. AI

IMPACT Accelerates inference for hierarchical language models, potentially improving efficiency in various NLP tasks.

RANK_REASON This is a research paper detailing a new method for language models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Abraham Toluwase Owodunni
• arXiv
• CatalyzeX
• CORE Recommender
• DagsHub
• Gotit.pub
• hierarchical language models
• Hugging Face
• multi-byte prediction
• Multi Token Prediction
• ScienceCast

• paper
• infra

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →