New method compresses Transformer inputs by predicting and removing redundant bytes

Researchers have developed a novel method to compress input sequences for Transformer models by utilizing an autoregressive byte language model. This approach identifies and removes easily predictable bytes from the input, reducing computational cost and sequence length without sacrificing translation quality. The technique has demonstrated effectiveness across multiple language pairs, including English-French, Finnish-English, Russian-English, and Chinese-English, achieving compression ratios between 0.47 and 0.67 while maintaining or improving translation performance. AI

IMPACT Reduces computational costs and improves efficiency for Transformer models, potentially accelerating adoption of byte-level tokenization.

RANK_REASON The cluster contains a research paper detailing a new method for compressing Transformer inputs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• arXiv
• Autoregressive byte language model
• Byte-level tokenization
• Chinese- English Parallel Texts for International Exhibition Publicity:a Comparison of Rhetoric and Translation Modes
• English--French
• Finnish--English
• machine translation
• Russian--English
• Transformer++

• paper
• infra

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