Retentive Network - A Successor to Transformer for Large Language Models

Explore a comprehensive analysis of the Retentive Network (RetNet), a groundbreaking alternative to Transformer architecture for large language models. Delve into the innovative retention mechanism that achieves training parallelism while maintaining low-cost inference. Examine the impossible triangle concept, parallel versus sequential processing, and the intricacies of the retention mechanism. Investigate chunkwise and multi-scale retention techniques, compare RetNet to other architectures, and review experimental evaluations demonstrating its promising performance. Gain insights into how RetNet addresses key challenges in sequence modeling, potentially revolutionizing the field of large language models.

- Intro
- The impossible triangle
- Parallel vs sequential
- Retention mechanism
- Chunkwise and multi-scale retention
- Comparison to other architectures
- Experimental evaluation