CADENCE: Adaptive decoding method to speed up text generation
In brief
- CADENCE generates token sequences in parallel using continuous latent vectors.
- An adaptive router adjusts prediction length based on context.
- Up to 1.3x faster than standard decoding on multiple benchmarks.
A new decoding method, CADENCE, enables parallel generation of token sequences by predicting continuous latent vectors. It dynamically adjusts prediction length based on context, improving efficiency by up to 1.3x compared to standard decoding. Tests on Llama-3.1-8B-Instruct show significant gains across multiple benchmarks.
This summary gives you the essentials. The full article, its figures and quotes are on cs.AI updates on arXiv.org.
Read the original articleAI-generated summary of the source, checked by rules. Report an error