Accelerating Text Generation: A New Self-Speculative Decoding Method

Researchers have introduced a novel technique called self-speculative decoding that significantly speeds up text generation in large language models. Unlike traditional methods that rely on separate draft models, this approach uses the model itself to generate speculative tokens, then verifies them in parallel. This reduces the need for multiple forward passes and cuts generation latency by up to 50%. The method maintains output quality while improving efficiency, making it promising for real-time applications.