How to Guide Your Language Flow
Apple research lab ships probe guidance — a new method to steer diffusion language models without extra inference cost.
Why it matters
Academic research on diffusion model guidance techniques. Demonstrates Apple's continued work on language generation methods, with potential implications for efficient model steering in production systems.
The key facts
10 to knowMethod: probe guidance uses frozen internal states from existing diffusion models
Eliminates need for additional forward pass at inference time
Benchmarked on continuous diffusion language models
Achieves state-of-the-art on unconditional generation task
Published by Apple Machine Learning Research
Method: 'probe guidance' uses frozen internal states from existing diffusion models
Applied to continuous diffusion language models
Achieves new state-of-the-art on unconditional generation
Similar principle to autoguidance but with reduced computational cost
Source: Apple Machine Learning Research
Go to the source
Apple Machine Learningmachinelearning.apple.com
Publisher excerpt: We introduce a new method to guide flow matching models. Our approach, which we call probe guidance, uses the frozen internal states of an existing diffusion model to construct a guidance signal. This works using a similar principle as autoguidance, but eliminates the need for an additional forward…