Auto Regressive Decoding

Autoregressive decoding, the sequential generation of text in large language models (LLMs), faces significant efficiency challenges due to its inherent computational demands. Current research focuses on accelerating this process through techniques like speculative decoding, which involves parallel generation of multiple candidate text sequences followed by verification, and employing architectural optimizations such as multiple decoding heads or early-exiting mechanisms. These advancements aim to reduce latency and memory consumption, improving the practicality of LLMs for real-world applications, particularly in resource-constrained environments. The ultimate goal is to enable faster and more efficient LLM inference without sacrificing the quality of generated text.